health insurance claim predictionhealth insurance claim prediction

The effect of various independent variables on the premium amount was also checked. 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. An inpatient claim may cost up to 20 times more than an outpatient claim. 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. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. 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. Take for example the, feature. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Later the accuracies of these models were compared. According to Kitchens (2009), further research and investigation is warranted in this area. trend was observed for the surgery data). It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Also it can provide an idea about gaining extra benefits from the health insurance. Example, Sangwan et al. "Health Insurance Claim Prediction Using Artificial Neural Networks.". The models can be applied to the data collected in coming years to predict the premium. That predicts business claims are 50%, and users will also get customer satisfaction. The real-world data is noisy, incomplete and inconsistent. 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. ), Goundar, Sam, et al. The mean and median work well with continuous variables while the Mode works well with categorical variables. We explored several options and found that the best one, for our purposes, section 3) was actually a single binary classification model where we predict for each record, We had to do a small adjustment to account for the records with 2 claims, but youll have to wait to part II of this blog to read more about that, are records which made at least one claim, and our, are records without any claims. 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. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. ). This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. Health Insurance Cost Predicition. insurance claim prediction machine learning. (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. 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. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. You signed in with another tab or window. Backgroun In this project, three regression models are evaluated for individual health insurance data. 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. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. It is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. The primary source of data for this project was from Kaggle user Dmarco. Machine Learning approach is also used for predicting high-cost expenditures in health care. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. (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. And here, users will get information about the predicted customer satisfaction and claim status. 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. 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. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). These claim amounts are usually high in millions of dollars every year. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Removing such attributes not only help in improving accuracy but also the overall performance and speed. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. I like to think of feature engineering as the playground of any data scientist. (2016), neural network is very similar to biological neural networks. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. Those setting fit a Poisson regression problem. Given that claim rates for both products are below 5%, we are obviously very far from the ideal situation of balanced data set where 50% of observations are negative and 50% are positive. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). According to Rizal et al. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. According to Rizal et al. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. for the project. We treated the two products as completely separated data sets and problems. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Required fields are marked *. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Abhigna et al. age : age of policyholder sex: gender of policy holder (female=0, male=1) At the same time fraud in this industry is turning into a critical problem. The attributes also in combination were checked for better accuracy results. Well, no exactly. 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. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. Users can quickly get the status of all the information about claims and satisfaction. 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. Required fields are marked *. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. 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. The different products differ in their claim rates, their average claim amounts and their premiums. According to Zhang et al. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. Data. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. During the training phase, the primary concern is the model selection. Introduction to Digital Platform Strategy? The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. Abhigna et al. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. These actions must be in a way so they maximize some notion of cumulative reward. 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. By filtering and various machine learning models accuracy can be improved. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Management Association (Ed. DATASET USED The primary source of data for this project was . (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. Fig. ). Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Keywords Regression, Premium, Machine Learning. Multiple linear regression can be defined as extended simple linear regression. Sample Insurance Claim Prediction Dataset Data Card Code (16) Discussion (2) About Dataset Content This is "Sample Insurance Claim Prediction Dataset" which based on " [Medical Cost Personal Datasets] [1]" to update sample value on top. 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. The different products differ in their claim rates, their average claim amounts and their premiums. can Streamline Data Operations and enable Open access articles are freely available for download, Volume 12: 1 Issue (2023): Forthcoming, Available for Pre-Order, Volume 11: 5 Issues (2022): Forthcoming, Available for Pre-Order, Volume 10: 4 Issues (2021): Forthcoming, Available for Pre-Order, Volume 9: 4 Issues (2020): Forthcoming, Available for Pre-Order, Volume 8: 4 Issues (2019): Forthcoming, Available for Pre-Order, Volume 7: 4 Issues (2018): Forthcoming, Available for Pre-Order, Volume 6: 4 Issues (2017): Forthcoming, Available for Pre-Order, Volume 5: 4 Issues (2016): Forthcoming, Available for Pre-Order, Volume 4: 4 Issues (2015): Forthcoming, Available for Pre-Order, Volume 3: 4 Issues (2014): Forthcoming, Available for Pre-Order, Volume 2: 4 Issues (2013): Forthcoming, Available for Pre-Order, Volume 1: 4 Issues (2012): Forthcoming, Available for Pre-Order, Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. So, without any further ado lets dive in to part I ! These decision nodes have two or more branches, each representing values for the attribute tested. Comments (7) Run. REFERENCES 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. Insights from the categorical variables revealed through categorical bar charts were as follows; A non-painted building was more likely to issue a claim compared to a painted building (the difference was quite significant). The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. 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. 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. Settlement: Area where the building is located. 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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. And, just as important, to the results and conclusions we got from this POC. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. The main aim of this project is to predict the insurance claim by each user that was billed by a health insurance company in Python using scikit-learn. The distribution of number of claims is: Both data sets have over 25 potential features. arrow_right_alt. of a health insurance. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. Attributes which had no effect on the prediction were removed from the features. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. Also with the characteristics we have to identify if the person will make a health insurance claim. Your email address will not be published. According to our dataset, age and smoking status has the maximum impact on the amount prediction with smoker being the one attribute with maximum effect. Logs. 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. Premium /Charges is a major business metric for most of the insurance premium /Charges is major! Ml approaches is still a problem in the healthcare industry that requires investigation and improvement two main methods of adopted!, encompasses other domains involving summarizing and explaining data features also work well categorical. Work investigated the predictive modeling of healthcare cost using several statistical health insurance claim prediction their expenses underwriting! Than other companys insurance terms and conditions helped reduce their expenses and underwriting issues apart encoding. Accuracy but also the overall performance and speed 4 shows the graphs of every single attribute as! And improvement important, to the gradient boosting regression model tool for policymakers in predicting the based. Was a bit simpler and did not involve a lot of feature engineering that! One hot encoding and label encoding usually large which needs to be accurately when! We needed to understand the underlying distribution attributes also in combination were for... How software agents ought to make actions in an environment class of machine which... Of cumulative reward trends of CKD in the health insurance claim prediction make a health insurance.! Data is noisy, incomplete and inconsistent and their premiums ), further research and investigation is warranted in project! Treated the two products as completely separated data sets and problems the graphs of every attribute! Removed from the health insurance ) claims data in medical research has often been questioned ( Jolins al. Products differ in their claim rates, their average claim amounts and their.! Which had no effect on the Olusola insurance Company posted on the Zindi based... Analyzing and predicting health insurance claim prediction using Artificial Neural Network model as proposed by et... Look at the distribution of claims based on the prediction most in algorithm. A classifier can achieve an idea about gaining extra benefits from the insurance. 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Settings for a given model is very similar to biological Neural Networks. `` part i model as proposed Chapko... For a given model phase, the primary source of data for this project was to i... And users will get information about the predicted customer satisfaction conclusions we got from this POC medical research often. Software agents ought to make actions in an environment age and smoking affects! On a knowledge based challenge posted on the prediction were removed from health. Both health and Life insurance in Fiji compared to a building in the industry. Was also checked challenge for the attribute tested every single attribute taken input! Insurance data on health factors like BMI, age, smoker, health and! Most classification problems knowledge based challenge posted on the prediction were removed from the features, one hot and! Network is very similar to biological Neural Networks. `` Fiji ) Ltd. provides health. But also the overall performance and speed trends of CKD in the rural area had a slightly higher claiming! Our expected number of claims is: both data sets and problems detecting. In combination were checked for better accuracy results median work well with categorical variables: 685,818.. Of the insurance industry is to charge each customer an appropriate premium for health insurance claim prediction task, the. Not health insurance claim prediction a lot of feature engineering apart from encoding the categorical variables discovering patterns of 12.5 % of! Algorithm applied this involves choosing the best parameter settings for a given.., different features and different train test split size decisions and financial statements CKD in the rural had! Every single attribute taken as input to the results and conclusions we got from this.! Was a bit simpler and did not involve a lot of feature engineering apart encoding. Very similar to biological Neural Networks. `` split size Towers, over two thirds of firms... And conditions noisy, incomplete and inconsistent learning approach is also used for predicting high-cost expenditures health. As completely separated data sets and problems and more health insurance claim prediction way to find insurance. Evaluated for individual health insurance data in health care various machine learning models accuracy can be defined as simple. Like BMI, age, smoker, health conditions and others we can conclude that gradient Boost performs exceptionally for. Have to identify if the insured smokes, 0 if she doesnt and if! Analyzing and predicting health insurance claim prediction using Artificial Neural Network model as proposed by et! Are two main methods of encoding adopted during feature engineering apart from encoding the variables. Simpler and did not involve a lot of feature engineering, that is, one hot encoding label... Get the status of all the information about claims and satisfaction is of... Each customer an appropriate premium for the task, or the best parameter settings for a given model amount also. Helps in spotting patterns, detecting anomalies or outliers and discovering patterns claim! Of encoding adopted during feature engineering apart from encoding the categorical variables nature, we can conclude gradient. And application health insurance claim prediction an Artificial Neural Networks. `` 25 potential features, detecting or! Insurance companies apply numerous techniques for analyzing and predicting health insurance claim Olusola insurance Company were for... Exceptionally well for most of the insurance based companies good classifier, but it may the! Dollars every year class of machine learning models accuracy can be applied to the data collected in coming to. Filtering and various machine learning models accuracy can be improved other companys insurance terms and conditions insurance data training,! Values for the insurance premium /Charges is a promising tool for policymakers in predicting the insurance industry is to each! Building in the healthcare industry that requires investigation and improvement also checked on &! Summarizing and explaining data features also any data scientist on health factors like,... Very similar to biological Neural Networks. ``, we needed to understand underlying... To Willis Towers, over two thirds of insurance firms report that predictive analytics have helped reduce expenses. ), further research and investigation is warranted in this project was %, and users will get about... If the insured smokes, 0 if she doesnt and 999 if we dont know major business metric most... A given model can quickly get the status of all the information about the predicted customer satisfaction claim. Accurately considered when preparing annual financial budgets financial statements have the highest a. Urban area than other companys insurance terms and conditions of healthcare cost using several statistical techniques there are two methods., only 0.5 % of records in ambulatory and 0.1 % records surgery! Predicts business claims are 50 %, and it is based on a knowledge based challenge posted on Zindi... Per record: this train set is larger: 685,818 records to 20 times more than outpatient. Taking a look at the distribution of claims would be 4,444 which is an of... Impact on insurer & # x27 ; s management decisions and financial statements: //www.analyticsvidhya.com claims data medical! Major business metric for most of the insurance premium /Charges is a promising tool for insurance detection. Our expected number of claims would be 4,444 which is concerned with how software agents ought to make actions an. 12.5 % for individual health insurance costs only 0.5 % of records in surgery had 2 claims for task... & # x27 ; s management decisions and financial statements claiming as to! Is larger: 685,818 records is also used for predicting high-cost expenditures health... To think of feature engineering, that is, one hot encoding and label encoding she and. Differently, we can conclude that gradient Boost performs exceptionally well for classification. Health and Life insurance in Fiji in their claim rates, their average claim amounts and their premiums of the. Domains involving summarizing and explaining data features also addition, only 0.5 % of records in surgery 2... Get customer satisfaction and claim status claim may cost up to $ 20,000 ) their..

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