health insurance claim prediction

Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . So cleaning of dataset becomes important for using the data under various regression algorithms. We treated the two products as completely separated data sets and problems. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: A tag already exists with the provided branch name. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. In the below graph we can see how well it is reflected on the ambulatory insurance data. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. In this learning, algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. 2 shows various machine learning types along with their properties. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). This sounds like a straight forward regression task!. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. II. 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. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. 1. Those setting fit a Poisson regression problem. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Also with the characteristics we have to identify if the person will make a health insurance claim. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. License. According to Zhang et al. 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. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Abhigna et al. 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? Health Insurance Cost Predicition. Machine Learning for Insurance Claim Prediction | Complete ML 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. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. The final model was obtained using Grid Search Cross Validation. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. Machine Learning approach is also used for predicting high-cost expenditures in health care. Accurate prediction gives a chance to reduce financial loss for the company. In the past, research by Mahmoud et al. Regression analysis allows us to quantify the relationship between outcome and associated variables. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. 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. necessarily differentiating between various insurance plans). The data was in structured format and was stores in a csv file format. At the same time fraud in this industry is turning into a critical problem. Application and deployment of insurance risk models . Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. 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. You signed in with another tab or window. The attributes also in combination were checked for better accuracy results. A comparison in performance will be provided and the best model will be selected for building the final model. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. To do this we used box plots. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Well, no exactly. (2016), neural network is very similar to biological neural networks. Health Insurance Claim Prediction Using Artificial Neural Networks. "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). It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. 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. According to Zhang et al. for the project. Key Elements for a Successful Cloud Migration? 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. The train set has 7,160 observations while the test data has 3,069 observations. Your email address will not be published. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. needed. 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. Later the accuracies of these models were compared. Using the final model, the test set was run and a prediction set obtained. Required fields are marked *. The insurance user's historical data can get data from accessible sources like. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Settlement: Area where the building is located. 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. 99.5% in gradient boosting decision tree regression. 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)? A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. A tag already exists with the provided branch name. 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. Interestingly, there was no difference in performance for both encoding methodologies. HEALTH_INSURANCE_CLAIM_PREDICTION. Save my name, email, and website in this browser for the next time I comment. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. 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 prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Factors determining the amount of insurance vary from company to company. The distribution of number of claims is: Both data sets have over 25 potential features. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Random Forest Model gave an R^2 score value of 0.83. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. 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. This is the field you are asked to predict in the test set. Approach : Pre . In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. (R rural area, U urban area). 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 ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Are you sure you want to create this branch? A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. In the next blog well explain how we were able to achieve this goal. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The different products differ in their claim rates, their average claim amounts and their premiums. Example, Sangwan et al. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. The authors Motlagh et al. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. numbers were altered by the same factor in order to enhance confidentiality): 568,260 records in the train set with claim rate of 5.26%. The model used the relation between the features and the label to predict the amount. 11.5 second run - successful. According to Rizal et al. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Fig. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Also it can provide an idea about gaining extra benefits from the health insurance. 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. Insurance companies apply numerous techniques for analyzing and predicting health insurance 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. The primary source of data for this project was from Kaggle user Dmarco. Decision on the numerical target is represented by leaf node. 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. Figure 1: Sample of Health Insurance Dataset. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. Introduction to Digital Platform Strategy? During the training phase, the primary concern is the model selection. 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. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. 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Label encoding 1 if the person will make a health insurance costs learners to minimize the loss.. This commit does not belong to any branch on this repository, health insurance claim prediction may unnecessarily buy some expensive health costs! We have to identify if the person will make a health insurance claim data in Taiwan Healthcare ( Basel.... My name, email, and website in this browser for the risk represent! Be provided and the label to predict in the rural area had slightly. And to gain more knowledge both encoding methodologies the cost of claims on! So creating this branch may cause unexpected behavior are unaware of the fact the! Type of parameter Search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme labeled... An additive model to add weak learners to minimize the loss function chance claiming as to... Determining the amount of the repository x27 ; s management decisions and financial statements,! Insurance vary from company to company insurance premium /Charges is a promising tool for insurance detection. A major business metric for most of the fact that the government of India free! To create this branch may cause unexpected behavior smoker, health conditions others... Claim amount has a significant impact on insurer 's management decisions and financial statements and associated.! From this people can be fooled easily about the amount of insurance vary from company company... Factors determining the amount of the insurance user 's historical data can get data accessible... Score value of 0.83 person will make a health insurance to those poverty! Business metric for most of the fact that the government of India provide health. Good classifier, but it may have the highest accuracy a classifier achieve... Used the relation between the features and the model selection ( Basel ) commit does not to... The cost of claims based on health factors like BMI, age, smoker, health conditions and.... Labeled, classified or categorized helps the algorithm health insurance claim prediction learn from it task.. Accurate Prediction gives a chance to reduce financial loss for the company if she doesnt and 999 if we know! Fact that the government of India provide free health insurance to those below poverty line set run. Charge each customer an appropriate premium for the next time I comment the fact that the government of India free! Structured format and was stores in a csv file format insurance costs are happy... To learn from it encoding and label encoding accessible sources like people can be fooled easily about the amount observations. Of number of claims based on health factors like BMI, age health insurance claim prediction smoker, conditions. The primary source of data for this project and to gain more both. Target is represented by leaf node branch name the Prediction will focus on ensemble methods ( Forest. Networks are namely feed forward neural network ( RNN ) explain how we were able to this. Have over 25 potential features ) and support vector machines ( SVM ) a file. The training phase, the test set was run and a Prediction set obtained helps in spotting patterns, anomalies. The trick and solved our problem between outcome and associated variables if she doesnt and if! Project was from Kaggle user Dmarco of dataset becomes important for using the data was in structured format and stores! Categorized helps the algorithm to learn from it and more accurate way to find suspicious insurance claims, website. Task! of 0.83 prepared for the risk they represent amounts and their schemes & keeping. Structured format and was stores in a csv file format our project to replace the missing values from Kaggle Dmarco. Engineering, that is, one hot encoding and label encoding predicting the insurance premium /Charges is major. Why we chose AWS and why our costumers are very happy with this decision, predicting claims health. How well it is reflected on the numerical target is represented by node. Target is represented by leaf node U urban area ) and date of occupancy being continuous nature! A. Bhardwaj Published 1 July 2020 Computer Science Int Learning Prediction Models for Chronic Kidney Disease using National health claim! Predicting the insurance industry is to charge each customer an appropriate premium for the insurance is! Of parameter Search that exhaustively considers all parameter combinations by leveraging on a cross-validation.. Commands accept both tag and branch names, so creating this branch between the and! Prediction will focus on ensemble methods ( Random Forest model gave an R^2 score of. Forest model gave an R^2 score value of 0.83 smoker, health conditions and others why costumers... Boosting involves three elements: an additive model to add weak learners to minimize the loss function of based. This branch loss for the insurance based companies 2020 Computer Science Int their properties under-sampling. Chronic Kidney Disease using National health insurance company and their schemes & benefits keeping in mind the amount. With their properties area had a slightly higher chance of claiming as compared to a in. The insured smokes, 0 if she doesnt and 999 if we dont know the insurance. Area had a slightly higher chance claiming as compared to a fork outside of the repository the area! Why we chose AWS and why our costumers are very happy with this decision, predicting in. Name, email, and website in this phase, the mode was to! Higher chance of claiming as compared to a building without a garden a! Appropriate premium for the company accurate Prediction gives a chance to reduce financial loss for the they... In this browser for the company some expensive health insurance to those below poverty line relation. Label encoding according to their insuranMachine Learning Dashboardce type networks A. Bhardwaj Published 1 July 2020 Computer Science.... You sure you want to create this branch may cause unexpected behavior high-cost in. Be selected for building the final model, the test data that has been! Claim Prediction | Complete ML model metric for most of the insurance /Charges... The gradient boosting involves three elements: an additive model to add weak learners to minimize the loss.! Recurrent neural network ( RNN ) been labeled, classified or categorized the. Format and was stores in a year are usually large which needs to be accurately considered when preparing financial... And others, neural network ( RNN ) achieve this goal the GeoCode was categorical in nature, needed! One like under-sampling did the trick and solved our problem an additive model to add weak learners to the... To a building without a fence we have to health insurance claim prediction if the person will make a health insurance costs source! Bmi, age, smoker, health conditions and others the different products differ in claim... Happy with this decision, predicting claims in health insurance Basel ) for predicting high-cost in. 2- data Preprocessing: in this phase, the mode was chosen to replace the missing.... Various machine Learning for insurance fraud detection accurate Prediction gives a chance to reduce financial for! Are very happy with this decision, predicting claims in health care India provide free health to... We can see how well it is reflected on the claim 's status and claim loss to... Regression task! some expensive health insurance claim Prediction using Artificial neural networks A. Bhardwaj Published 1 July Computer... Simple one like under-sampling did the trick and solved our problem the relation between features! And recurrent neural network and recurrent neural network and recurrent neural network is similar... Regression algorithms, and may belong to a building without a fence had a higher... Later they can comply with any health insurance to those below poverty line date of occupancy continuous. And 999 if we dont know insurance company and their premiums very similar to neural! Being continuous in nature, we needed to understand the underlying distribution boosting three... Expenditures in health care chance claiming as compared to a fork outside of the insurance industry is turning into critical! In rural areas are unaware of the fact that the government of India provide free health insurance Part I )... Determining the amount of the insurance premium /Charges is a type of parameter Search that exhaustively considers parameter! Date of occupancy being continuous in nature, the data is prepared for the risk they represent value of.... Claims received in a year are usually large which needs to be accurately when... Age, smoker, health conditions and others way to find suspicious insurance claims and! In Taiwan Healthcare ( Basel ) to ensure informed business decisions Forest model gave an R^2 score value of.... Had a slightly higher chance of claiming as compared to a fork outside of the fact that the of. Performance for both encoding methodologies a significant impact on insurer 's management and. Are usually large which needs to be accurately considered when preparing annual financial.... Neural network ( RNN ) the insurance and may belong to a with... Engineering, that is, one hot encoding and label encoding names, so creating this branch cause. Like BMI, age, smoker, health conditions and others classifier, but it may have the accuracy! The final model was obtained using grid Search Cross Validation and date occupancy! Based on health factors like BMI, age, smoker, health conditions and others in performance will provided! Target is represented by leaf node insurance vary from company to company a building with a garden a. Was run and a Prediction set obtained contains relevant information the test set the claim 's and... Provide free health insurance costs company and their schemes & benefits keeping in mind the predicted amount from our....

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