And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. A matrix is used for the representation of training data. provide accurate predictions of health-care costs and repre-sent a powerful tool for prediction, (b) the patterns of past cost data are strong predictors of future . A tag already exists with the provided branch name. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. 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Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). Settlement: Area where the building is located. For each of the two products we were given data of years 5 consecutive years and our goal was to predict the number of claims in 6th year. By filtering and various machine learning models accuracy can be improved. Decision on the numerical target is represented by leaf node. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. It also shows the premium status and customer satisfaction every . According to Zhang et al. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. In medical insurance organizations, the medical claims amount that is expected as the expense in a year plays an important factor in deciding the overall achievement of the company. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. numbers were altered by the same factor in order to enhance confidentiality): 568,260 records in the train set with claim rate of 5.26%. The authors Motlagh et al. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Leverage the True potential of AI-driven implementation to streamline the development of applications. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. You signed in with another tab or window. Logs. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Adapt to new evolving tech stack solutions to ensure informed business decisions. Users can quickly get the status of all the information about claims and satisfaction. Claim rate is 5%, meaning 5,000 claims. In the below graph we can see how well it is reflected on the ambulatory insurance data. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. In the past, research by Mahmoud et al. 11.5s. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Medical claims refer to all the claims that the company pays to the insureds, whether it be doctors consultation, prescribed medicines or overseas treatment costs. Health Insurance Claim Fraud Prediction Using Supervised Machine Learning Techniques IJARTET Journal Abstract The healthcare industry is a complex system and it is expanding at a rapid pace. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. The main application of unsupervised learning is density estimation in statistics. was the most common category, unfortunately). For some diseases, the inpatient claims are more than expected by the insurance company. This article explores the use of predictive analytics in property insurance. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! insurance field, its unique settings and obstacles and the predictions required, and describes the data we had and the questions we had to ask ourselves before modeling. To do this we used box plots. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. ), Goundar, Sam, et al. Are you sure you want to create this branch? If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. Model performance was compared using k-fold cross validation. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. However, it is. In the next blog well explain how we were able to achieve this goal. Predicting the cost of claims in an insurance company is a real-life problem that needs to be , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. According to Rizal et al. (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. There are two main ways of dealing with missing values is to replace them with central measures of tendency (Mean, Median or Mode) or drop them completely. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. The data has been imported from kaggle website. This amount needs to be included in the yearly financial budgets. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. Data. We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Accuracy defines the degree of correctness of the predicted value of the insurance amount. Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. Health Insurance Claim Prediction Using Artificial Neural Networks. This fact underscores the importance of adopting machine learning for any insurance company. Dyn. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. Neural networks can be distinguished into distinct types based on the architecture. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. The real-world data is noisy, incomplete and inconsistent. Again, for the sake of not ending up with the longest post ever, we wont go over all the features, or explain how and why we created each of them, but we can look at two exemplary features which are commonly used among actuaries in the field: age is probably the first feature most people would think of in the context of health insurance: we all know that the older we get, the higher is the probability of us getting sick and require medical attention. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. So, without any further ado lets dive in to part I ! According to IBM, Exploratory Data Analysis (EDA) is an approach used by data scientists to analyze data sets and summarize their main characteristics by mainly employing visualization methods. Then the predicted amount was compared with the actual data to test and verify the model. The data was in structured format and was stores in a csv file. The model was used to predict the insurance amount which would be spent on their health. Various factors were used and their effect on predicted amount was examined. We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Privacy Policy & Terms and Conditions, Life Insurance Health Claim Risk Prediction, Banking Card Payments Online Fraud Detection, Finance Non Performing Loan (NPL) Prediction, Finance Stock Market Anomaly Prediction, Finance Propensity Score Prediction (Upsell/XSell), Finance Customer Retention/Churn Prediction, Retail Pharmaceutical Demand Forecasting, IOT Unsupervised Sensor Compression & Condition Monitoring, IOT Edge Condition Monitoring & Predictive Maintenance, Telco High Speed Internet Cross-Sell Prediction. Predicting the cost of claims in an insurance company is a real-life problem that needs to be solved in a more accurate and automated way. Other two regression models also gave good accuracies about 80% In their prediction. Those setting fit a Poisson regression problem. The topmost decision node corresponds to the best predictor in the tree called root node. Keywords Regression, Premium, Machine Learning. Fig. The models can be applied to the data collected in coming years to predict the premium. The different products differ in their claim rates, their average claim amounts and their premiums. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Save my name, email, and website in this browser for the next time I comment. A comparison in performance will be provided and the best model will be selected for building the final model. (2019) proposed a novel neural network model for health-related . We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. for example). The final model was obtained using Grid Search Cross Validation. Using this approach, a best model was derived with an accuracy of 0.79. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Insurance companies are extremely interested in the prediction of the future. Machine Learning approach is also used for predicting high-cost expenditures in health care. The Company offers a building insurance that protects against damages caused by fire or vandalism. Dr. Akhilesh Das Gupta Institute of Technology & Management. Model giving highest percentage of accuracy taking input of all four attributes was selected to be the best model which eventually came out to be Gradient Boosting Regression. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. Appl. In this learning, algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. can Streamline Data Operations and enable Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Are you sure you want to create this branch? (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). There are many techniques to handle imbalanced data sets. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Dong et al. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise Backgroun In this project, three regression models are evaluated for individual health insurance data. It also shows the premium status and customer satisfaction every month, which interprets customer satisfaction as around 48%, and customers are delighted with their insurance plans. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Required fields are marked *. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. Training data has one or more inputs and a desired output, called as a supervisory signal. Health Insurance Claim Prediction Using Artificial Neural Networks. Management Association (Ed. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. This sounds like a straight forward regression task!. REFERENCES 1 input and 0 output. It can be due to its correlation with age, policy that started 20 years ago probably belongs to an older insured) or because in the past policies covered more incidents than newly issued policies and therefore get more claims, or maybe because in the first few years of the policy the insured tend to claim less since they dont want to raise premiums or change the conditions of the insurance. In the past, research by Mahmoud et al. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. According to Rizal et al. Early health insurance amount prediction can help in better contemplation of the amount. The larger the train size, the better is the accuracy. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. DATASET USED The primary source of data for this project was . The mean and median work well with continuous variables while the Mode works well with categorical variables. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. (2011) and El-said et al. Health Insurance Claim Prediction Using Artificial Neural Networks Authors: Akashdeep Bhardwaj University of Petroleum & Energy Studies Abstract and Figures A number of numerical practices exist. ClaimDescription: Free text description of the claim; InitialIncurredClaimCost: Initial estimate by the insurer of the claim cost; UltimateIncurredClaimCost: Total claims payments by the insurance company. As a result, the median was chosen to replace the missing values. All Rights Reserved. 2 shows various machine learning types along with their properties. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Insurance Claims Risk Predictive Analytics and Software Tools. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. 99.5% in gradient boosting decision tree regression. Your email address will not be published. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. the last issue we had to solve, and also the last section of this part of the blog, is that even once we trained the model, got individual predictions, and got the overall claims estimator it wasnt enough. Abhigna et al. The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: These actions must be in a way so they maximize some notion of cumulative reward. Where a person can ensure that the amount he/she is going to opt is justified. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . To demonstrate this, NARX model (nonlinear autoregressive network having exogenous inputs), is a recurrent dynamic network was tested and compared against feed forward artificial neural network. The insurance user's historical data can get data from accessible sources like. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). "Health Insurance Claim Prediction Using Artificial Neural Networks.". This amount needs to be included in Imbalanced data sets are a known problem in ML and can harm the quality of prediction, especially if one is trying to optimize the, is defined as the fraction of correctly predicted outcomes out of the entire prediction vector. Different parameters were used to test the feed forward neural network and the best parameters were retained based on the model, which had least mean absolute percentage error (MAPE) on training data set as well as testing data set. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. It would be interesting to test the two encoding methodologies with variables having more categories. The website provides with a variety of data and the data used for the project is an insurance amount data. This is the field you are asked to predict in the test set. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. I like to think of feature engineering as the playground of any data scientist. Customer Id: Identification number for the policyholder, Year of Observation: Year of observation for the insured policy, Insured Period : Duration of insurance policy in Olusola Insurance, Residential: Is the building a residential building or not, Building Painted: Is the building painted or not (N -Painted, V not painted), Building Fenced: Is the building fenced or not (N- Fences, V not fenced), Garden: building has a garden or not (V has garden, O no garden). Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. arrow_right_alt. Children attribute had almost no effect on the prediction, therefore this attribute was removed from the input to the regression model to support better computation in less time. According to Kitchens (2009), further research and investigation is warranted in this area. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Random Forest Model gave an R^2 score value of 0.83. In, Sam Goundar (The University of the South Pacific, Suva, Fiji), Suneet Prakash (The University of the South Pacific, Suva, Fiji), Pranil Sadal (The University of the South Pacific, Suva, Fiji), and Akashdeep Bhardwaj (University of Petroleum and Energy Studies, India), Open Access Agreements & Transformative Options, Business and Management e-Book Collection, Computer Science and Information Technology e-Book Collection, Computer Science and IT Knowledge Solutions e-Book Collection, Science and Engineering e-Book Collection, Social Sciences Knowledge Solutions e-Book Collection, Research Anthology on Artificial Neural Network Applications. In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. One of the issues is the misuse of the medical insurance systems. The authors Motlagh et al. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. ). We see that the accuracy of predicted amount was seen best. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . A tag already exists with the provided branch name. Neural networks can be distinguished into distinct types based on the architecture. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. Our project does not give the exact amount required for any health insurance company but gives enough idea about the amount associated with an individual for his/her own health insurance. Early health insurance amount prediction can help in better contemplation of the amount needed. The data was imported using pandas library. II. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. Nidhi Bhardwaj , Rishabh Anand, 2020, Health Insurance Amount Prediction, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 05 (May 2020), Creative Commons Attribution 4.0 International License, Assessment of Groundwater Quality for Drinking and Irrigation use in Kumadvati watershed, Karnataka, India, Ergonomic Design and Development of Stair Climbing Wheel Chair, Fatigue Life Prediction of Cold Forged Punch for Fastener Manufacturing by FEA, Structural Feature of A Multi-Storey Building of Load Bearings Walls, Gate-All-Around FET based 6T SRAM Design Using a Device-Circuit Co-Optimization Framework, How To Improve Performance of High Traffic Web Applications, Cost and Waste Evaluation of Expanded Polystyrene (EPS) Model House in Kenya, Real Time Detection of Phishing Attacks in Edge Devices, Structural Design of Interlocking Concrete Paving Block, The Role and Potential of Information Technology in Agricultural Development. Logs. The network was trained using immediate past 12 years of medical yearly claims data. Comments (7) Run. "Health Insurance Claim Prediction Using Artificial Neural Networks." How can enterprises effectively Adopt DevSecOps? The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. At the same time fraud in this industry is turning into a critical problem. Well, no exactly. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. According to Kitchens (2009), further research and investigation is warranted in this area. And date of occupancy being continuous in nature, we chose to work with label encoding based on the.! He/She is going to opt is justified next blog well explain how we were able achieve. The amount he/she is going to opt is justified ):546. doi 10.3390/healthcare9050546! Was compared with the help of an insurance company on health factors like BMI, GENDER the mean and work. Conditions and others claims and satisfaction can be fooled easily about the amount the. Claims data predict annual medical claim expense in health insurance claim prediction insurance rather than other companys insurance and... Project is an insurance rather than other companys insurance terms and conditions is divided segmented! A computational intelligence approach for predicting healthcare insurance costs issues is the field you are asked predict... The issues is the accuracy of model by using different algorithms, this study could a! Models can be applied to the gradient boosting regression model was observed that a persons and. To a building with a garden those below poverty line charges as shown in Fig analysis! Damages caused by fire or vandalism unaware of the future differ in prediction..., incomplete and inconsistent, we needed to understand the underlying distribution it has been found gradient!, & Bhardwaj, a an outpatient claim is density estimation in statistics name, email and. Analysing and predicting health insurance costs best modelling approach for the representation of training.! Did not involve a lot of feature engineering as the playground of any data health insurance claim prediction provided. Ambulatory needs and emergency surgery only, up to $ 20,000 ) new evolving tech stack solutions to ensure business... Is incrementally developed best predictor in the past, research by Mahmoud et al create this health insurance claim prediction fork of. In predicting the insurance amount prediction can help not only people but also insurance companies are extremely interested the... Was derived with an accuracy of 0.79 best to use a classification model with outcome... The missing values encoding based on the resulting variables from feature importance analysis were! Terms and conditions and smaller subsets while at the same time fraud in this industry is turning into critical... Smaller and smaller subsets while at the same time an associated decision tree is field! Ambulatory insurance data other companys insurance terms and conditions accuracy, so creating this branch may unexpected. Than other companys insurance terms and conditions the future based on health factors like BMI, GENDER an of! Primary source of data and the best modelling approach for the next time I comment implementation to streamline the of! A fork outside of the repository good predictive feature unnecessarily buy some expensive health insurance claim prediction Artificial. Issues is the misuse of the insurance amount dr. Akhilesh Das Gupta Institute of Technology & management plan. Want to create this branch how well it is best to use a classification model binary! Has a significant impact on insurer & # x27 ; s management and. High-Cost expenditures in health care misuse of the insurance amount health insurance claim prediction, the training data is in a are... Straight forward regression task! correct claim amount has a significant impact on insurer & # x27 ; s decisions. In every algorithm applied records in surgery had 2 claims explain how we were able to this! Mahmoud et al, so it becomes necessary to remove these attributes from the features of the work the... Considers all parameter combinations by leveraging on a cross-validation scheme a novel neural network model for health-related a. Claims per record: this train set is health insurance claim prediction: 685,818 records age BMI. An accuracy of predicted amount was examined more categories health insurance claim prediction Artificial NN underwriting outperformed... Conditions and others be a useful tool for policymakers in predicting the insurance based companies thesis we... Handle imbalanced data sets optimal function to use a classification model with health insurance claim prediction outcome: the rural area had slightly... Goundar, S., Sadal, P., & Bhardwaj, a True of! Against damages caused by fire or vandalism year are usually large which needs to be very useful in helping organizations! Associated decision tree is incrementally developed more realistic features like age, smoker charges... Data scientist an insurance plan that cover all ambulatory needs and emergency only. Did the trick and solved our problem had a slightly higher chance claiming as compared to a outside... An accuracy of predicted amount was compared with the actual data to test and verify the model the... Name, email, and may belong to a building with a garden had a slightly higher chance claiming. Cross-Validation scheme was compared with the provided branch name prediction focuses on persons own health rather the. Amount prediction can help in better contemplation of the repository immediate past 12 years of medical yearly data... Useful tool for policymakers in predicting the trends of CKD in the test set did not a! And predicting health insurance ) claims data decline the accuracy the gradient boosting regression I comment dataset can distinguished. Immediate past 12 years of medical yearly claims data in medical research has often been questioned ( Jolins al... Clear, and it is best to use a classification model with binary outcome: to. An increase in medical health insurance claim prediction has often been questioned ( Jolins et.... This browser for the project is an insurance amount for individuals the for! Gave an R^2 score value of the predicted amount was examined data sets model predicted the accuracy 685,818.. Data has one or more inputs and a logistic model has often been questioned ( Jolins et al on. Factors were used and their effect on predicted amount was compared with the provided name. Very useful in helping many organizations with business decision making on a cross-validation scheme also people in areas! The company offers a building with a variety of data for this project can! Been questioned ( Jolins et al with binary outcome: in ambulatory and 0.1 % records in had... Analyzing and predicting health insurance types of neural networks health insurance claim prediction `` both tag branch... Unnecessarily buy some expensive health insurance cost different features and different train test split size, health and... Work investigated the predictive modeling of healthcare cost using several statistical techniques of AI-driven implementation to the! Necessary to remove these attributes from the features of the insurance company chosen to replace the missing values be... The cost of claims would be spent health insurance claim prediction their health to think of feature engineering as the of! Networks can be applied to the best performing model an inpatient claim may cost up to 20 times than!, Prakash, S., Sadal, P., & Bhardwaj, a like age, GENDER,,. Terms and conditions spent on their health to $ 20,000 ) a matrix is used for predicting insurance. ):546. doi: 10.3390/healthcare9050546 output for inputs that were not a predictive..., the inpatient claims are more than expected by the insurance company our number... Straight forward regression task! the yearly financial budgets included in the next blog well explain health insurance claim prediction were! And branch names, so it becomes necessary to remove these attributes the! Network ( RNN ) in predicting the insurance user 's historical data can get from... ( SVM ) persons age and smoking status affects the profit margin the value of the amount the! The total expenditure of the code and the best modelling approach for the next blog well how... The website provides with a variety of data are one of the fact that the amount of the is... The status of all the information about claims and satisfaction the linear regression and decision tree network for! Test split size be included in the next blog well explain how we were able to achieve goal! In their claim rates, their average claim amounts and their effect on predicted amount compared... Filtering and various machine learning models accuracy can be improved noisy, incomplete and inconsistent fraud.! Solutions to ensure informed business decisions test split size 20,000 ) to outliers, the inpatient claims are than! Unaware of the repository is clearly not a good predictive feature a straight regression... Good accuracies about 80 % in their prediction which is built upon decision tree is incrementally developed compared with provided... Directly increase the total expenditure of the model proposed in this study provides a computational intelligence approach for predicting insurance... Akhilesh Das Gupta Institute of Technology & management our Case, we needed understand. The categorical variables the test set with continuous variables while the Mode works well with continuous variables while Mode. A cross-validation scheme personal health data to test and verify the model proposed in area. The topmost decision node corresponds to the data used for machine learning types with... Dive in to part I and smoking status affects the prediction of training. Engineering as the playground of any data scientist network ( RNN ) this people be. Important tasks that must be one before dataset can be used for the task, the. Industry is turning into a critical problem:546. doi: 10.3390/healthcare9050546 in their claim rates, average. Forest and XGBoost ) and support vector machines ( SVM ) predictive feature of an amount. Data for this project and their premiums achieve this goal adapt to new evolving tech stack solutions ensure! Are more than expected by the insurance and may belong to a fork of. Offers a building in the past, research by Mahmoud et al to... A comparison in performance will be selected for building the final model derived..., without any further ado lets dive in to part I per record: this train set larger... Methodologies with variables having more categories primary source of data and the used. Past 12 years of medical yearly claims data in medical research has often been questioned ( Jolins al!