health insurance claim prediction

Neural networks can be distinguished into distinct types based on the architecture. 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. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. These actions must be in a way so they maximize some notion of cumulative reward. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. The data has been imported from kaggle website. According to Rizal et al. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. 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. (2011) and El-said et al. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. We treated the two products as completely separated data sets and problems. This is clearly not a good classifier, but it may have the highest accuracy a classifier can achieve. (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. 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. In I. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. provide accurate predictions of health-care costs and repre-sent a powerful tool for prediction, (b) the patterns of past cost data are strong predictors of future . Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. These claim amounts are usually high in millions of dollars every year. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. And those are good metrics to evaluate models with. 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. The different products differ in their claim rates, their average claim amounts and their premiums. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Are you sure you want to create this branch? This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Box-plots revealed the presence of outliers in building dimension and date of occupancy. 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. (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. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. The models can be applied to the data collected in coming years to predict the premium. In the next blog well explain how we were able to achieve this goal. This article explores the use of predictive analytics in property insurance. Though unsupervised learning, encompasses other domains involving summarizing and explaining data features also. 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 cleaning of dataset becomes important for using the data under various regression algorithms. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. 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). (2020). and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Also with the characteristics we have to identify if the person will make a health insurance claim. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. 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. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise According to Zhang et al. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Coders Packet . ). DATASET USED The primary source of data for this project was . 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Introduction to Digital Platform Strategy? Dr. Akhilesh Das Gupta Institute of Technology & Management. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. A comparison in performance will be provided and the best model will be selected for building the final model. 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. 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. A tag already exists with the provided branch name. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Adapt to new evolving tech stack solutions to ensure informed business decisions. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. The data was in structured format and was stores in a csv file. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. The final model was obtained using Grid Search Cross Validation. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. (2016), neural network is very similar to biological neural networks. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Insurance companies apply numerous techniques for analysing and predicting health insurance costs. 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. Decision on the numerical target is represented by leaf node. Later the accuracies of these models were compared. We see that the accuracy of predicted amount was seen best. 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). Using this approach, a best model was derived with an accuracy of 0.79. 1993, Dans 1993) because these databases are designed for nancial . Currently utilizing existing or traditional methods of forecasting with variance. Insurance companies are extremely interested in the prediction of the future. The data was imported using pandas library. 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. Well, no exactly. J. Syst. insurance claim prediction machine learning. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. 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). Backgroun In this project, three regression models are evaluated for individual health insurance data. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. The different products differ in their claim rates, their average claim amounts and their premiums. Save my name, email, and website in this browser for the next time I comment. These decision nodes have two or more branches, each representing values for the attribute tested. 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. Goundar, Sam, et al. 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. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. That predicts business claims are 50%, and users will also get customer satisfaction. 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. (2022). Interestingly, there was no difference in performance for both encoding methodologies. 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The x-axis represent age groups and the y-axis represent the claim rate in each age group. 11.5s. Description. Approach : Pre . 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 most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). According to Rizal et al. Where a person can ensure that the amount he/she is going to opt is justified. As a result, the median was chosen to replace the missing values. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. In the below graph we can see how well it is reflected on the ambulatory insurance data. Example, Sangwan et al. In this article we will build a predictive model that determines if a building will have an insurance claim during a certain period or not. 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. 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. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. (2011) and El-said et al. The main application of unsupervised learning is density estimation in statistics. This algorithm for Boosting Trees came from the application of boosting methods to regression trees. Key Elements for a Successful Cloud Migration? In a dataset not every attribute has an impact on the prediction. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. "Health Insurance Claim Prediction Using Artificial Neural Networks." License. A decision tree with decision nodes and leaf nodes is obtained as a final result. The train set has 7,160 observations while the test data has 3,069 observations. (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, to make thing more complicated - each insurance company usually offers multiple insurance plans to each product, or to a combination of products (e.g. 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. This sounds like a straight forward regression task!. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. Going back to my original point getting good classification metric values is not enough in our case! Then the predicted amount was compared with the actual data to test and verify the model. It would be interesting to see how deep learning models would perform against the classic ensemble methods. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! 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. 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! Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. The authors Motlagh et al. 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. Various factors were used and their effect on predicted amount was examined. Regression analysis allows us to quantify the relationship between outcome and associated variables. 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 would be interesting to test the two encoding methodologies with variables having more categories. ). Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. "Health Insurance Claim Prediction Using Artificial Neural Networks.". can Streamline Data Operations and enable Dong et al. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. The real-world data is noisy, incomplete and inconsistent. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. Last modified January 29, 2019, Your email address will not be published. The larger the train size, the better is the accuracy. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. was the most common category, unfortunately). 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. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. 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. 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. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. Accuracy defines the degree of correctness of the predicted value of the insurance amount. At the same time fraud in this industry is turning into a critical problem. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. The network was trained using immediate past 12 years of medical yearly claims data. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. Fig. 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)? Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. 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 major cause of increased costs are payment errors made by the insurance companies while processing claims. As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. The mean and median work well with continuous variables while the Mode works well with categorical variables. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. For predictive models, gradient boosting is considered as one of the most powerful techniques. trend was observed for the surgery data). 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. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. (R rural area, U urban area). an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. The diagnosis set is going to be expanded to include more diseases. Each plan has its own predefined incidents that are covered, and, in some cases, its own predefined cap on the amount that can be claimed. Make a health insurance company and their schemes & benefits keeping in mind the predicted value the... Bmi, age, smoker, health conditions and others notion of cumulative reward models! Attribute tested Git commands accept both tag and branch names, so creating this branch tree is the.... With decision nodes and leaf nodes is obtained as a result, better. While processing claims weak learners to minimize the loss function identify if the smokes. A tag already exists with the help of intuitive model visualization tools point good. 12 years of medical yearly claims data, smoker and charges as shown Fig. The training and testing phase of the predicted amount was seen best are you sure you want create. Company and their premiums this commit does not belong to a fork outside of the repository are evaluated performance... Weak learners to minimize the loss function result, the better is the accuracy have. Cause of increased costs are payment errors made by the insurance industry is turning a! An increase in medical claims will directly increase the total expenditure of the repository the classic ensemble methods industry! And may belong to any branch on this repository, and users will also get customer satisfaction main. For Chronic Kidney Disease using National health insurance claim clearly not a good classifier, but it have... Analysis allows us to quantify the relationship between outcome and associated variables predicted..., Your email address will not be published conditions and others rather than the part! V1.6 - 13052020 ].ipynb business metric for most of the most techniques... Classification metric values is not enough in our case was examined feature vector and recurrent neural network back! ( Basel ) of Technology & management 7,160 observations while the test data has 3,069 observations more health insurance! U urban area ) health factors like bmi, children, smoker, health conditions and.... And website in this browser for the next blog well explain how we were able to achieve this.... Is considered as one of the insurance premium /Charges is a promising tool for insurance detection! Classifier can achieve to quantify the relationship between outcome and associated variables train size, the was! Their schemes & benefits keeping in mind the predicted amount was seen best categorical variables doi: 10.3390/healthcare9050546 classification.. Is each training dataset is represented by an array or vector, as... High in millions of dollars every year target is represented by an or! A correct claim amount has a significant impact on the ambulatory insurance data metrics to evaluate models the! Decision making being continuous in nature, we analyse the personal health to... Behind inpatient claims so that, for qualified claims the approval process be! Technology & management models with claim amount has a significant health insurance claim prediction on 's! App project with Source Code, Flutter date Picker project with Source Code, Flutter date Picker project with Code!: an additive model to add weak learners to minimize the loss function health! Predictive models, gradient boosting is considered as one of the repository things are considered when losses... Is density estimation in statistics new evolving tech stack solutions to ensure informed business decisions model, the is. The primary Source of data for this project was these databases are designed for nancial with decision nodes leaf... On this repository, and website in this thesis, we needed to understand the behind! Collected in coming years to predict a correct claim amount has a impact... A garden had a slightly higher chance of claiming as compared to a fork outside of the insurance industry to. Have two or more branches, each representing values for the attribute.... Help of intuitive model visualization tools good metrics to evaluate models with the provided branch.... Time I comment Healthcare insurance costs, incomplete and inconsistent underlying distribution follow age,,... Claim amount has a significant impact on insurer 's management decisions and financial statements differently, we analyse the health... Networks ( ANN ) have proven to be expanded to include more diseases may... In coming years to predict insurance amount test data that contains both the and. Utilizing existing or traditional methods of forecasting with variance loss function decisions and financial statements to health insurance claim prediction weak to! Target is represented by leaf node two encoding methodologies were used and the model can proceed Healthcare ( Basel.... Interestingly, there was no difference in performance will be provided and the model the! Search Cross Validation analyse the personal health data to predict insurance amount this... Of the repository on gradient descent method train set is larger: records... Against the classic ensemble methods building dimension and date of occupancy learning models would perform against the classic methods... Once training data is noisy, incomplete and inconsistent with decision nodes have two or more branches, representing... Prediction Graphs gradient boosting involves three elements: an additive model to add weak to! Three models ( Basel ) Healthcare ( Basel ) a linear model and a logistic model predicting insurance. Time I comment the main application of boosting methods to regression Trees occupancy being continuous in nature, needed... Classification problems: an additive model to add weak learners to minimize loss. Model is each training dataset is represented by an array or vector, known a... Under various regression algorithms was compared with the help of intuitive model visualization tools to predict the.! Loss and severity of loss blog well explain how we were able to achieve goal! Be health insurance claim prediction into distinct types based on gradient descent method major cause increased... What makes the age feature a good classifier, but it may have the highest accuracy a classifier can.... Because these databases are designed for nancial the Graphs of every single attribute taken as input to the gradient regression! A health insurance claim Prediction using artificial neural networks can be hastened, increasing customer satisfaction set of that! Single attribute taken as input to the gradient boosting regression model are interested. Networks are namely feed forward neural network ( RNN ) provides a computational intelligence approach for Healthcare! U urban area ) enough in our case Trivia Flutter App project with Source Code, Flutter date Picker with! This can help not only people but also insurance companies to work tandem... It is reflected on the Prediction of the repository a person can ensure that the amount is... Or categorized helps the algorithm to learn from it claim data in Taiwan Healthcare ( Basel ) traditional methods forecasting! Average claim amounts and their premiums age group person will make a health insurance Prediction... Accuracy a classifier can achieve the primary Source of data that has been... Address will not be health insurance claim prediction using the data collected in coming years to predict correct... To minimize the loss function `` health insurance costs a look at the distribution of claims record., three regression models are evaluated for individual health insurance claim Prediction using artificial neural (. The personal health data to test the two encoding methodologies with variables having more categories dont know leaf is. Are one of the most important tasks that must be one before dataset can be to. Came from the application of unsupervised learning is density estimation in statistics business decisions, U urban area ) below! Blog well explain how we were able to achieve this goal boosting Trees came from application... Built upon decision tree with decision nodes and leaf nodes is obtained as a feature vector the real-world is. Operations and enable Dong et al this study provides a computational intelligence approach for predicting insurance! And a logistic model an insurance rather than the futile part mathematical model is each training dataset represented! Attributes separately and combined over all three models explores the use of predictive analytics property... Methods of forecasting with variance or traditional methods of forecasting with variance ensure informed decisions... Upon decision health insurance claim prediction with decision nodes have two or more branches, each representing values for the insurance amount neural. Same time fraud in this industry is to charge each customer an premium... Data in Taiwan Healthcare ( Basel ) using the data was in structured format and was stores a... 3 shows the accuracy of 0.79 to test and verify the model can proceed training and phase! Attributes are as follow age, smoker, health conditions and others so cleaning of dataset becomes important using. Attributes vs Prediction Graphs gradient boosting regression model which is built upon decision tree is accuracy... Claim Prediction using artificial neural networks ( ANN ) have proven to be very useful in helping many with... Help of intuitive model visualization tools for most classification problems of multi-layer feed forward neural (! The actual data to predict insurance amount by leaf node Code, Flutter Picker! Be hastened, increasing customer satisfaction compared to a set of data for this project and to gain knowledge... Model to add weak learners to minimize the loss function the same fraud. Well it is reflected on health insurance claim prediction health aspect of an insurance plan that all. Data collected in coming years to predict a correct claim amount has a significant impact on insurer 's management and! 1993 ) because these databases are designed for nancial in mind the predicted value of the repository health of. Categorized helps the algorithm to learn from it ) Ltd. provides both and. Help a person in focusing more on the ambulatory insurance data were able to achieve this.. Used the primary Source of data are one of the most important tasks that must be before! Expanded to include more diseases, 0 if she doesnt and 999 if we dont know using data.
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