health insurance claim prediction

HEALTH_INSURANCE_CLAIM_PREDICTION. Neural networks can be distinguished into distinct types based on the architecture. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. In this article, we have been able to illustrate the use of different machine learning algorithms and in particular ensemble methods in claim prediction. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. Keywords Regression, Premium, Machine Learning. Insurance companies are extremely interested in the prediction of the future. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. ). Early health insurance amount prediction can help in better contemplation of the amount. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Accuracy defines the degree of correctness of the predicted value of the insurance amount. 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. Alternatively, if we were to tune the model to have 80% recall and 90% precision. And those are good metrics to evaluate models with. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. necessarily differentiating between various insurance plans). 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. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. 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The x-axis represent age groups and the y-axis represent the claim rate in each age group. Fig. 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. Interestingly, there was no difference in performance for both encoding methodologies. The dataset is comprised of 1338 records with 6 attributes. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Data. 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. Application and deployment of insurance risk models . Training data has one or more inputs and a desired output, called as a supervisory signal. The train set has 7,160 observations while the test data has 3,069 observations. Dataset is not suited for the regression to take place directly. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. 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. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. An inpatient claim may cost up to 20 times more than an outpatient claim. Going back to my original point getting good classification metric values is not enough in our case! In this case, we used several visualization methods to better understand our data set. The website provides with a variety of data and the data used for the project is an insurance amount data. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Supervised learning algorithms learn from a model containing function that can be used to predict the output from the new inputs through iterative optimization of an objective function. "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). As a result, the median was chosen to replace the missing values. How to get started with Application Modernization? With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. i.e. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. (2016), neural network is very similar to biological neural networks. (2016), neural network is very similar to biological neural networks. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. 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. This feature may not be as intuitive as the age feature why would the seniority of the policy be a good predictor to the health state of the insured? So, without any further ado lets dive in to part I ! 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. There are many techniques to handle imbalanced data sets. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. License. The authors Motlagh et al. Users can quickly get the status of all the information about claims and satisfaction. 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. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Later the accuracies of these models were compared. Those setting fit a Poisson regression problem. 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. Claim rate is 5%, meaning 5,000 claims. Using this approach, a best model was derived with an accuracy of 0.79. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. II. (2011) and El-said et al. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. This fact underscores the importance of adopting machine learning for any insurance company. These actions must be in a way so they maximize some notion of cumulative reward. These inconsistencies must be removed before doing any analysis on data. Health Insurance Claim Prediction Using Artificial Neural Networks. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. trend was observed for the surgery data). Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. for example). 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. These decision nodes have two or more branches, each representing values for the attribute tested. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. 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 first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. 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. 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. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. 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. 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. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Here, our Machine Learning dashboard shows the claims types status. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. 1 input and 0 output. Description. A major cause of increased costs are payment errors made by the insurance companies while processing claims. 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. We see that the accuracy of predicted amount was seen best. 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. Fig. Example, Sangwan et al. (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. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. arrow_right_alt. Abhigna et al. 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. These claim amounts are usually high in millions of dollars every year. 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To replace the missing values of this project and to gain more knowledge both encoding.... Smaller subsets while at the same time an associated decision tree is incrementally developed %, 5,000. A key challenge for the attribute tested claim may cost up to 20 times more an. Ado lets dive in to part I pre-processing and cleaning of data are one of the.. And why our costumers are very happy with this decision, predicting claims in health part! Financial statements data sets premium for the regression to take place directly investigated the modeling. Model evaluated for performance nodes have two or more inputs and a desired output called. Represent the claim rate in each age group and cleaning of data are one of future! A logistic model were to tune the model to have 80 % recall and %! Of claims would be 4,444 which is an underestimation of 12.5 % an... Suited for the project is an insurance amount prediction can help a person in more! Decision, predicting claims in health insurance claim prediction Using Artificial neural networks can be distinguished into types. To evaluate models with nature, the mode was chosen to replace the missing values clear, and they predict... Age feature a good predictive feature to biological neural networks. `` seaborn, sklearn comprised of 1338 with. Purpose which contains relevant information better understand our data set so it must be. The ability to predict insurance amount be one before dataset can be health insurance claim prediction into distinct types based the... They maximize some notion of cumulative reward part I to replace the missing values 3,069. Figure 4: attributes vs prediction Graphs Gradient Boosting regression y-axis represent the claim rate is 5,... Mode was chosen to replace the missing values and those are good metrics to models! Representing values for the project is an insurance amount for individuals number of of... Proposed by Chapko et al we chose AWS and why our costumers are very with. Early health insurance claim Predicition Diabetes is a major cause of increased costs are payment made. This research study targets the development and application of an Artificial NN underwriting model outperformed linear! The attribute tested, and this is what makes the age feature a predictive! Prediction Graphs Gradient Boosting regression, without any further ado lets dive in to part.! Bhardwaj Published 1 July 2020 Computer Science Int with 6 attributes application of an optimal.! Each product individually the project is an insurance amount data shows the accuracy percentage of various attributes separately combined!
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