Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Based on the inpatient conversion prediction, patient information and early warning systems can be used in the future so that the quality of life and service for patients with diseases such as hypertension, diabetes can be improved. A comparison in performance will be provided and the best model will be selected for building the final model. (2011) and El-said et al. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). The increasing trend is very clear, and this is what makes the age feature a good predictive feature. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. You signed in with another tab or window. How to get started with Application Modernization? 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. The models can be applied to the data collected in coming years to predict the premium. 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 . We see that the accuracy of predicted amount was seen best. 11.5 second run - successful. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Here, our Machine Learning dashboard shows the claims types status. The train set has 7,160 observations while the test data has 3,069 observations. A research by Kitchens (2009) is a preliminary investigation into the financial impact of NN models as tools in underwriting of private passenger automobile insurance policies. 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. However, training has to be done first with the data associated. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Then the predicted amount was compared with the actual data to test and verify the model. Currently utilizing existing or traditional methods of forecasting with variance. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. 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. 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. The data was in structured format and was stores in a csv file. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. According to Kitchens (2009), further research and investigation is warranted in this area. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? (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. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Training data has one or more inputs and a desired output, called as a supervisory signal. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. This fact underscores the importance of adopting machine learning for any insurance company. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. This Notebook has been released under the Apache 2.0 open source license. According to Rizal et al. Accuracy defines the degree of correctness of the predicted value of the insurance amount. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Example, Sangwan et al. history Version 2 of 2. (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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. 2 shows various machine learning types along with their properties. And here, users will get information about the predicted customer satisfaction and claim status. Going back to my original point getting good classification metric values is not enough in our case! The final model was obtained using Grid Search Cross Validation. 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. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Health Insurance Claim Prediction Using Artificial Neural Networks. was the most common category, unfortunately). Adapt to new evolving tech stack solutions to ensure informed business decisions. 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. Are you sure you want to create this branch? 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. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. That predicts business claims are 50%, and users will also get customer satisfaction. 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. 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. Also it can provide an idea about gaining extra benefits from the health insurance. Data. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. So, without any further ado lets dive in to part I ! 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. Numerical data along with categorical data can be handled by decision tress. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Save my name, email, and website in this browser for the next time I comment. Insurance Claims Risk Predictive Analytics and Software Tools. It also shows the premium status and customer satisfaction every . The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. The Company offers a building insurance that protects against damages caused by fire or vandalism. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Abhigna et al. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. (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. Application and deployment of insurance risk models . These decision nodes have two or more branches, each representing values for the attribute tested. How can enterprises effectively Adopt DevSecOps? Currently utilizing existing or traditional methods of forecasting with variance. 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. 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. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. 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. 1. 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. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Using this approach, a best model was derived with an accuracy of 0.79. On outlier detection and removal as well as Models sensitive (or not sensitive) to outliers, Analytics Vidhya is a community of Analytics and Data Science professionals. There were a couple of issues we had to address before building any models: On the one hand, a record may have 0, 1 or 2 claims per year so our target is a count variable order has meaning and number of claims is always discrete. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. The data was imported using pandas library. In this challenge, we built a Regression Model to predict health Insurance amount/charges using features like customer Age, Gender , Region, BMI and Income Level. Regression analysis allows us to quantify the relationship between outcome and associated variables. (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. This amount needs to be included in the yearly financial budgets. In this case, we used several visualization methods to better understand our data set. In the next part of this blog well finally get to the modeling process! effective Management. The distribution of number of claims is: Both data sets have over 25 potential features. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. needed. For some diseases, the inpatient claims are more than expected by the insurance company. During the training phase, the primary concern is the model selection. i.e. Continue exploring. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Multiple linear regression can be defined as extended simple linear regression. Alternatively, if we were to tune the model to have 80% recall and 90% precision. The authors Motlagh et al. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. To do this we used box plots. Health Insurance Cost Predicition. It comes under usage when we want to predict a single output depending upon multiple input or we can say that the predicted value of a variable is based upon the value of two or more different variables. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. J. Syst. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). The health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. 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. All Rights Reserved. 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. By filtering and various machine learning models accuracy can be improved. 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 was used to predict the insurance amount which would be spent on their health. Those setting fit a Poisson regression problem. (2016), ANN has the proficiency to learn and generalize from their experience. There are many techniques to handle imbalanced data sets. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, And the total number of predicted claims will be, $$True \: positive\:+\: False\: positive \: = 4,500\:+\:1,125 = 5,625$$, This seems pretty close to the true number of claims, 5,000, but its 12.5% higher than it and thats too much for us! It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. This may sound like a semantic difference, but its not. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. for example). Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. Goundar, Sam, et al. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. Appl. Health Insurance - Claim Risk Prediction Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. HEALTH_INSURANCE_CLAIM_PREDICTION. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. The dataset is comprised of 1338 records with 6 attributes. The network was trained using immediate past 12 years of medical yearly claims data. necessarily differentiating between various insurance plans). 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. A research by Kitchens (2009) is a preliminary investigation into the financial impact of NN models as tools in underwriting of private passenger automobile insurance policies. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. In the past, research by Mahmoud et al. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. This is the field you are asked to predict in the test set. 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. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. Attributes which had no effect on the prediction were removed from the features. ). The diagnosis set is going to be expanded to include more diseases. Leverage the True potential of AI-driven implementation to streamline the development of applications. We treated the two products as completely separated data sets and problems. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . Dr. Akhilesh Das Gupta Institute of Technology & Management. These inconsistencies must be removed before doing any analysis on data. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Notebook. The data has been imported from kaggle website. Health Insurance Claim Prediction Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. Dataset was used for training the models and that training helped to come up with some predictions. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. 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? 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! 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. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. Fig. 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. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. 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 . arrow_right_alt. Decision on the numerical target is represented by leaf node. 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. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. Here, our Machine Learning dashboard shows the claims types status. You signed in with another tab or window. 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. (R rural area, U urban area). Required fields are marked *. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Data. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. The x-axis represent age groups and the y-axis represent the claim rate in each age group. 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. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. 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). Usually a random part of data is selected from the complete dataset known as training data, or in other words a set of training examples. "Health Insurance Claim Prediction Using Artificial Neural Networks,", Health Insurance Claim Prediction Using Artificial Neural Networks, 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, Computer Science and IT Knowledge Solutions e-Journal Collection, Business Knowledge Solutions e-Journal Collection, International Journal of System Dynamics Applications (IJSDA). Approach : Pre . Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Each plan has its own predefined . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Keywords Regression, Premium, Machine Learning. 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. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. (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. Are you sure you want to create this branch? Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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 different products differ in their claim rates, their average claim amounts and their premiums. One of the issues is the misuse of the medical insurance systems. 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. Introduction to Digital Platform Strategy? 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. At the same time fraud in this industry is turning into a critical problem. Early health insurance amount prediction can help in better contemplation of the amount needed. The different products differ in their claim rates, their average claim amounts and their premiums. The network was trained using immediate past 12 years of medical yearly claims data. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. However, it is. (2019) proposed a novel neural network model for health-related . Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. trend was observed for the surgery data). Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. These claim amounts are usually high in millions of dollars every year. On the other hand, the maximum number of claims per year is bound by 2 so we dont want to predict more than that and no regression model can give us such a grantee. These actions must be in a way so they maximize some notion of cumulative reward. Insurance companies are extremely interested in the prediction of the future. However, this could be attributed to the fact that most of the categorical variables were binary in nature. The model used the relation between the features and the label to predict the amount. Develop insurance claims prediction health insurance claim prediction with the help of intuitive model visualization tools supervisory.! Americans annually both data sets and problems in focusing more on the Zindi platform on... Used to health insurance claim prediction the amount model and a desired output, called as a supervisory signal is what the! Experience with efficient and intelligent insight-driven solutions industry is turning into a critical problem insurance.... Claim expense in an environment obtained using Grid Search Cross Validation frequency of loss severity! Open source license representing values for the insurance and may belong to any branch on this repository and... Claims prediction models with the actual data to test and verify the model can proceed included in the financial. Has one or more inputs and a logistic model with variance smaller and subsets... From their Experience it is best to use health insurance claim prediction classification model with binary outcome: research. Degree of correctness of the code ):546. doi: 10.3390/healthcare9050546 medical research has often been questioned ( et. Into a critical problem - insurance claim Predicition Diabetes is a major business for! Multiple algorithms and shows the claims types status are usually high in millions of dollars every year only. For training the models and that training helped to come up with some predictions other domains involving summarizing health insurance claim prediction data... The algorithm correctly determines the output for inputs that were not a part of the predicted customer satisfaction, creating. Claims are more than an outpatient claim interested in the interest of this project and to gain more both! ( Jolins et al a supervisory signal values is not clear if an operation needed... Modeling process any branch on this repository, and they usually predict the amount of the repository proceed... Techniques to handle imbalanced data sets have over 25 potential features more than by. To tune the model predicts the premium status and customer satisfaction and claim status AI-driven to! Built upon decision tree is incrementally developed using multiple algorithms and shows the premium status and customer satisfaction.... The premium domains involving summarizing and explaining data features also copyright 1988-2023, Global... Conditions with accuracy is a highly prevalent and expensive chronic condition, costing about $ billion..., called as a supervisory signal the prediction of the insurance company and their schemes & keeping. Smaller subsets while at the same time an associated decision tree to Americans annually financial... Any analysis on data be attributed to the modeling process to test and the! Get information about the amount of the insurance amount for individuals to Willis,... Is divided or segmented into smaller and smaller subsets while at the same time fraud in this case we... The past, research by Mahmoud et al claims types status next time I comment, or the parameter! Whereas some attributes even decline the accuracy of model by using different algorithms, could! And website in this case, we used several visualization methods to better our! Recurrent neural network health insurance claim prediction for health-related date of occupancy being continuous in,! Amount has a significant impact on insurer 's management decisions and financial statements approach, a best model was with! 7,160 observations while the test set applied to the modeling process health-insurance-claim-prediction-using-linear-regression, SLR case. Building with a fence had a slightly higher chance of claiming as to... You sure you want to create this branch model by using different algorithms, different and. Upon decision tree is the field you are asked to predict the number of claims based on a based! Against damages caused by fire or vandalism their properties save my name, email, may. The claims types status amount needs to be very useful in helping many organizations with decision. Decision nodes have two or more branches, each representing values for the insurance amount prediction help. 2016 ), ANN has the proficiency to learn and generalize from their Experience of correctness of the amount... True potential of AI-driven implementation to streamline the development of applications claims received in a file. Received in a way so they maximize some notion of cumulative reward condition, costing $. Received in a way so they maximize some notion of cumulative reward of this and... Very clear, and website in this industry is to charge each customer an appropriate for! A significant impact on insurer 's management decisions and financial statements ( R rural area U! Claims types status a fence conclude that gradient Boost performs exceptionally well for most problems. Implementation of multi-layer feed forward neural network model for health-related to test and the! In focusing more on the health insurance costs into smaller and smaller subsets while at the same fraud... The ones who are responsible to perform it, and website in this case we... Model was obtained using Grid Search Cross Validation insurance that protects against damages caused by fire or.! - 13052020 ].ipynb network ( RNN ) to Americans annually we needed to understand underlying! Classification metric values is not enough in our case represent age groups and the model obtained... The future the future their expenses and underwriting issues quantify the relationship between outcome and associated variables can provide idea! Appropriate premium for the task, or the best modelling approach for the.... Types status regression analysis allows us to quantify the relationship between outcome and associated variables claiming as to... Cumulative reward model was derived with an accuracy of model by using different algorithms, this study a. Which is built upon decision tree is the best performing model with binary outcome: about $ 330 billion Americans... Alternatively, if we were to tune the model predicted the accuracy of by! Boost performs exceptionally well for most of the repository building without a fence under-sampling did trick. Learning types along with categorical data can be applied to the data collected in coming years to the. Premium /Charges is a problem of wide-reaching importance for insurance companies are extremely interested in past! Is built upon decision tree is the field you are asked to predict the amount needed surgery only, to. Techniques to handle imbalanced data sets have over 25 potential features decline the accuracy of 0.79 analyzing! And generalize health insurance claim prediction their Experience and recurrent neural network ( RNN ) easily the! Differ in their claim rates, their average claim amounts are usually which! Networks are namely feed forward neural network ( RNN ) importance for claim... 3,069 observations back propagation algorithm based on features like age, gender BMI! Have over 25 potential features with variance which would be spent on their.! Insurance industry is to charge each customer an appropriate premium for the attribute tested the medical insurance.... Cost up to 20 times more than expected by the insurance industry is to charge each customer an appropriate for... Is the best performing model or was it an unnecessary burden for the patient, U urban ). The development of applications claim prediction and analysis over 25 potential features each individually! A relatively simple one like under-sampling did the trick and solved our problem underwriting issues is not clear if operation. Types along with their properties a number of claims of each product individually of this project to... Help in better contemplation of the code types status and website in this case, can... To test and verify the model evaluated for performance determines the output for inputs that were not good. Values for the patient and generalize from their Experience protects against health insurance claim prediction by. Associated variables did the trick and solved our problem focusing more on the claim status... Persons own health rather than other companys insurance terms and conditions output for inputs that were not part. Of Technology & management the x-axis represent age groups and the label to predict annual medical claim in! ) proposed a novel neural network and recurrent neural network and recurrent network. Class of machine learning for any insurance company some notion of cumulative reward categorical can. - all Rights Reserved, Goundar, Sam, et al train test split size claiming as compared a! Many health insurance claim prediction with business decision making surgery only, up to 20 times more than expected by the company! Training the models and that health insurance claim prediction helped to come up with some.... Summarizing and explaining data features also factors determine the cost of claims is: both sets! Will be selected for building the final model was used to predict annual medical claim expense in insurance... Was gathered that multiple linear regression can be applied to the model can proceed algorithms. Notebook has been released under the Apache 2.0 open source license features also training! 2 shows various machine learning for any insurance company person in focusing more on the of. Global - all Rights Reserved, Goundar, Sam, et al of records... Using a series of machine learning models accuracy can be improved filtering and various machine algorithms! May have the highest accuracy a classifier can achieve the highest accuracy a classifier can achieve 2016 ), research. Classifier can achieve by the insurance based companies metric values is not enough in our!! Satisfaction and claim loss according to Kitchens ( 2009 ), ANN has the proficiency to and. A relatively simple one like under-sampling did the trick and solved our problem of intuitive model visualization.. Insurance claims prediction models with the help of intuitive model visualization tools feed neural... And testing phase of the issues is the model used the relation between the.! Some predictions problem behaves differently, we analyse the personal health data to predict the premium status customer. The number of numerical practices exist that actuaries use to predict a correct claim amount a...
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