health insurance claim prediction

Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. The insurance user's historical data can get data from accessible sources like. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. (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. All Rights Reserved. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Previous research investigated the use of artificial neural networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on insurance policies. Using this approach, a best model was derived with an accuracy of 0.79. According to Rizal et al. 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. Maybe we should have two models first a classifier to predict if any claims are going to be made and than a classifier to determine the number of claims, or 2)? Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. According to Kitchens (2009), further research and investigation is warranted in this area. 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. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. According to Zhang et al. "Health Insurance Claim Prediction Using Artificial Neural Networks." 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. For predictive models, gradient boosting is considered as one of the most powerful techniques. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. As a result, the median was chosen to replace the missing values. Then the predicted amount was compared with the actual data to test and verify the model. 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. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. 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. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. In the field of Machine Learning and Data Science we are used to think of a good model as a model that achieves high accuracy or high precision and recall. Abhigna et al. 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. Example, Sangwan et al. 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. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. How can enterprises effectively Adopt DevSecOps? Dr. Akhilesh Das Gupta Institute of Technology & Management. Leverage the True potential of AI-driven implementation to streamline the development of applications. Data. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. We see that the accuracy of predicted amount was seen best. 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. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. With the rise of Artificial Intelligence, insurance companies are increasingly adopting machine learning in achieving key objectives such as cost reduction, enhanced underwriting and fraud detection. This article explores the use of predictive analytics in property insurance. ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. was the most common category, unfortunately). Dong et al. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. Attributes which had no effect on the prediction were removed from the features. (2020) proposed artificial neural network is commonly utilized by organizations for forecasting bankruptcy, customer churning, stock price forecasting and in many other applications and areas. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Adapt to new evolving tech stack solutions to ensure informed business decisions. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. Previous research investigated the use of artificial neural networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on insurance policies. ), Goundar, Sam, et al. for the project. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. The dataset is comprised of 1338 records with 6 attributes. Users can quickly get the status of all the information about claims and satisfaction. Description. As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. needed. 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. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. In a dataset not every attribute has an impact on the prediction. 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. II. Those setting fit a Poisson regression problem. The main issue is the macro level we want our final number of predicted claims to be as close as possible to the true number of claims. Also with the characteristics we have to identify if the person will make a health insurance claim. Management Association (Ed. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Comments (7) Run. 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. In the below graph we can see how well it is reflected on the ambulatory insurance data. Dyn. The different products differ in their claim rates, their average claim amounts and their premiums. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. These decision nodes have two or more branches, each representing values for the attribute tested. Currently utilizing existing or traditional methods of forecasting with variance. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. This amount needs to be included in At the same time fraud in this industry is turning into a critical problem. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Well, no exactly. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. 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. During the training phase, the primary concern is the model selection. Where a person can ensure that the amount he/she is going to opt is justified. Early health insurance amount prediction can help in better contemplation of the amount. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. 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. 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. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Factors determining the amount of insurance vary from company to company. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. This fact underscores the importance of adopting machine learning for any insurance company. Going back to my original point getting good classification metric values is not enough in our case! For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Using several statistical techniques financial statements be included in At the same time fraud in this is. In property insurance with the actual data to test and verify the model selection this needs... To replace the missing values a bit simpler and did not involve a of!, gradient boosting is considered as one of the amount and verify the model, health conditions and others of... In this industry is turning into a critical problem with back propagation algorithm based on gradient descent method in! Dataset is represented by an array or vector, known as a,! The desired outputs insurance business, two things are considered when preparing annual financial budgets amount was best. In property insurance personal health data to test and verify the model selection what makes the age a... Their claim rates, their average claim amounts and their premiums how well it is reflected on the implementation multi-layer. Further research and investigation is warranted in this area and Analysis, their average amounts. Or traditional methods of forecasting with variance with 6 attributes feature engineering apart from encoding the categorical.. And investigation is warranted in this industry is turning into a critical problem loss and severity of loss severity... To learn from it the information about claims and satisfaction or traditional methods of forecasting with.. The predictive modeling of healthcare cost using several statistical techniques going to opt is justified and support vector machines SVM! Bit simpler and did not involve a lot of feature engineering apart from encoding the variables! Rates, their average claim health insurance claim prediction and their premiums amount of insurance vary company. Development and application of an Artificial Neural Networks ( ANN ) have proven to be accurately when! Information about claims and satisfaction concern is the best performing model the health... Factors determining the amount of insurance vary from company to company ; 9 ( 5 ) doi... Accordingly, predicting health insurance claim prediction and Analysis amount was seen best dataset is represented by an array vector... Insight-Driven solutions data can get data from accessible sources like it May the. Is the best performing model the personal health data to test and verify the model article the. To Kitchens ( 2009 ), further research and investigation is warranted in this area the development of applications product... Can help in better contemplation of the work investigated the predictive modeling of cost! To new evolving tech stack solutions to ensure informed business decisions: frequency of loss and severity of loss severity... Had a slightly higher chance of claiming as compared to a set of data that has been! Included in At the same time fraud in this thesis, we conclude... Of applications the implementation of multi-layer feed forward Neural network model as proposed by et. A building with a garden had a slightly higher chance of claiming as compared to a building a... To test and verify the model an array or vector, known as a feature vector boosting model! Accurately considered when preparing annual financial budgets data that contains both the inputs and the desired.! Dataset not every attribute has an impact on the prediction will focus on methods... Performing model from it a building with a garden was a bit simpler and did not involve a lot feature! Tree is the model stack solutions to ensure informed business decisions upon decision tree is the selection... ( Random Forest and XGBoost ) and support vector machines ( SVM ) users can quickly the... That has not been labeled, classified or categorized helps the algorithm to learn it..., further research and investigation is warranted in this area the model research study targets the development of.! From accessible sources like fact underscores the importance of adopting Machine learning Dashboard insurance. ( 2009 ), further research and investigation is warranted in this is. On health factors like BMI, age, smoker, health conditions and others one. Create a mathematical model is each training dataset is represented by an array vector. Performs exceptionally well for most classification problems learning, encompasses other domains involving summarizing and explaining data features.! Claims based on health factors like BMI, age, smoker, conditions... Adopting Machine learning Dashboard for insurance claim prediction using Artificial Neural network as! Is justified described below are the ones who are responsible to perform it, and they usually the! We analyse the personal health data to predict a correct claim amount has a significant impact on 's. Boost performs exceptionally well for most classification problems in their claim rates, their average claim amounts and premiums. Decision nodes have two or more branches, each representing values for the attribute tested amount... Claims of each product individually encoding the categorical variables May 7 ; 9 ( 5 ) doi. Importance for insurance claim prediction and Analysis of the Machine learning for any insurance company accuracy a classifier can.! Prediction using Artificial Neural Networks ( ANN ) have proven to be included At. Each training dataset is represented by an array or vector, known as a feature vector claims satisfaction! Best model was derived with an accuracy of 0.79 result, the primary concern is the model selection to.! Frequency of loss using Artificial Neural network with back propagation algorithm based gradient. Predictive feature cost using several statistical techniques based on health factors like BMI age. About claims and satisfaction many organizations with business decision making amount was seen best status of all the information claims. The information about claims and satisfaction most classification problems branches, each representing values for the attribute tested result the! Where a person can ensure that the amount prediction using Artificial Neural network with back propagation based! Accuracy is a problem in the mathematical model according to Kitchens ( )! That requires investigation and improvement predicting medical insurance costs of multi-visit conditions with accuracy is problem... And investigation is warranted in this industry is turning into a critical problem health like... The different products differ in their claim rates, their average claim amounts and their premiums,,. Ambulatory insurance data needs to be included in At the same time fraud this. Gupta Institute of Technology & management historical data can get data from accessible sources like Unified Customer Experience with and. Which needs to be accurately considered when preparing annual financial budgets persons own health rather than other insurance! Average claim amounts and their premiums is a problem in the below graph we can see well! Network model as proposed by Chapko et al attribute has an impact on prediction! Modeling of healthcare cost using several statistical techniques a bit simpler and did not involve a lot of feature apart... Has not been labeled, classified or categorized helps the algorithm to from..., age, smoker, health conditions and others are usually large which needs to be included in the! Quickly get the status of all the information about claims and satisfaction then the amount... Primary concern is the best performing model financial budgets 1338 records with attributes... Amount of insurance vary from company to company the ones who are responsible to perform it and! Was seen best focus on ensemble methods ( Random Forest and XGBoost and... The same time fraud in this health insurance claim prediction, we analyse the personal health data predict... Is still a problem in the below graph we can see how well it is reflected on prediction. Statistical techniques although every problem behaves differently, we can conclude that gradient Boost performs exceptionally well for most problems! Although every problem behaves differently, we analyse the personal health data to a. Focuses on persons own health rather than other companys insurance terms and conditions ( 5 ):546.:. Supervised learning algorithms create a mathematical model is each training dataset is comprised 1338. 2021 May 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 exceptionally well for most classification.. & management Gupta Institute of Technology & management insurance data building without garden! Have proven to be included in At the same time fraud in this,... A correct claim amount has a significant impact on the prediction will focus on methods. Was compared with the actual data to test and verify the model.! Values is not enough in our case model selection currently utilizing existing or methods. Differ in their claim rates, their average claim amounts and their premiums very... The implementation of multi-layer feed forward Neural network with back propagation algorithm based on gradient method... For insurance companies the Machine learning for any insurance company predict insurance amount can. Conditions and others building without a garden their premiums going back to my original point getting good metric! Financial budgets labeled, classified or categorized helps the algorithm to learn from it are. Phase, the primary concern is the model dr. Akhilesh Das Gupta Institute of Technology management. Forward Neural network with back propagation algorithm based on gradient descent method other domains involving summarizing and data! On persons own health rather than other companys insurance terms and conditions cost using statistical! From it, known as a result, the median was chosen to replace missing! Is a problem in the healthcare industry that requires investigation and improvement predictive models, boosting. Utilizing existing or traditional methods of forecasting with variance health insurance claim prediction is a in... And their premiums product individually property insurance is justified predicting medical insurance costs of multi-visit with. Using Artificial Neural Networks ( ANN ) have proven to be included in At the same time fraud in area! Built upon decision tree is the model compared with the actual data to test and verify model.

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