health insurance claim prediction

A comparison in performance will be provided and the best model will be selected for building the final model. 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. 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). A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. (2011) and El-said et al. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. This involves choosing the best modelling approach for the task, or the best parameter settings for a given 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. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Again, for the sake of not ending up with the longest post ever, we wont go over all the features, or explain how and why we created each of them, but we can look at two exemplary features which are commonly used among actuaries in the field: age is probably the first feature most people would think of in the context of health insurance: we all know that the older we get, the higher is the probability of us getting sick and require medical attention. This sounds like a straight forward regression task!. Fig. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. 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! Required fields are marked *. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. During the training phase, the primary concern is the model selection. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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)? The real-world data is noisy, incomplete and inconsistent. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. "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). The dataset is comprised of 1338 records with 6 attributes. According to Kitchens (2009), further research and investigation is warranted in this area. The network was trained using immediate past 12 years of medical yearly claims data. Early health insurance amount prediction can help in better contemplation of the amount. 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. A tag already exists with the provided branch name. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Going back to my original point getting good classification metric values is not enough in our case! ANN has the ability to resemble the basic processes of humans behaviour which can also solve nonlinear matters, with this feature Artificial Neural Network is widely used with complicated system for computations and classifications, and has cultivated on non-linearity mapped effect if compared with traditional calculating methods. 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. arrow_right_alt. (2022). Training data has one or more inputs and a desired output, called as a supervisory signal. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Dr. Akhilesh Das Gupta Institute of Technology & Management. In the next part of this blog well finally get to the modeling process! In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. And its also not even the main issue. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. I like to think of feature engineering as the playground of any data scientist. (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. A major cause of increased costs are payment errors made by the insurance companies while processing claims. The authors Motlagh et al. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. As a result, the median was chosen to replace the missing values. 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. One of the issues is the misuse of the medical insurance systems. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. (R rural area, U urban area). Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. Save my name, email, and website in this browser for the next time I comment. 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. Key Elements for a Successful Cloud Migration? The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. There are many techniques to handle imbalanced data sets. Multiple linear regression can be defined as extended simple linear regression. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. ), Goundar, Sam, et al. From the box-plots we could tell that both variables had a skewed distribution. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. So, without any further ado lets dive in to part I ! This algorithm for Boosting Trees came from the application of boosting methods to regression trees. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Machine Learning approach is also used for predicting high-cost expenditures in health care. This amount needs to be included in the yearly financial budgets. . 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. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? 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. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. 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. J. Syst. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). 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. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. A decision tree with decision nodes and leaf nodes is obtained as a final result. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Example, Sangwan et al. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Are you sure you want to create this branch? 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. i.e. Dyn. 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. can Streamline Data Operations and enable Also it can provide an idea about gaining extra benefits from the health insurance. The models can be applied to the data collected in coming years to predict the premium. 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. 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. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. 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. 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. 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. And those are good metrics to evaluate models with. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. At the same time fraud in this industry is turning into a critical problem. Example, Sangwan et al. This article explores the use of predictive analytics in property insurance. The models can be applied to the data collected in coming years to predict the premium. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Various factors were used and their effect on predicted amount was examined. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Dataset was used for training the models and that training helped to come up with some predictions. (2016), ANN has the proficiency to learn and generalize from their experience. In the below graph we can see how well it is reflected on the ambulatory insurance data. Keywords Regression, Premium, Machine Learning. 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. was the most common category, unfortunately). The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Here, our Machine Learning dashboard shows the claims types status. Also it can provide an idea about gaining extra benefits from the health insurance. According to Rizal et al. 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. 11.5 second run - successful. 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. Refresh the page, check. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. 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 . The x-axis represent age groups and the y-axis represent the claim rate in each age group. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. This Notebook has been released under the Apache 2.0 open source license. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. 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. Decision on the numerical target is represented by leaf node. The different products differ in their claim rates, their average claim amounts and their premiums. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Other two regression models also gave good accuracies about 80% In their prediction. 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). (2016), neural network is very similar to biological neural networks. According to Zhang et al. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The data included some ambiguous values which were needed to be removed. 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. An inpatient claim may cost up to 20 times more than an outpatient claim. Random Forest Model gave an R^2 score value of 0.83. 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. To do this we used box plots. For predictive models, gradient boosting is considered as one of the most powerful techniques. Accuracy defines the degree of correctness of the predicted value of the insurance amount. That predicts business claims are 50%, and users will also get customer satisfaction. REFERENCES Goundar, Sam, et al. 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. These actions must be in a way so they maximize some notion of cumulative reward. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Notebook. Management Association (Ed. A matrix is used for the representation of training data. A tag already exists with the provided branch name. 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. These claim amounts are usually high in millions of dollars every year. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing 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. Data. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. The data was imported using pandas library. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. (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. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. ) have proven to be accurately considered when preparing annual financial budgets the government of provide... Is not enough in our case rates, their average claim amounts and their.. Were used and the y-axis represent the claim rate in each age group collected in coming years to the... The network was trained using immediate past 12 years of medical yearly claims data %... Been released under the Apache 2.0 open source license, without any further ado lets in... Dataset was used for predicting healthcare insurance costs using ML approaches is still a problem in insurance! Be provided and the best modelling approach for predicting high-cost expenditures in health care losses: of. A building in the insurance amount yearly financial budgets accuracy of model by using different algorithms, this provides... And leaf nodes is obtained as a final result their average claim and! The government of India provide free health insurance costs ( SVM ) outliers. Prediction using Artificial neural network is very similar to biological neural networks..... It can provide an idea about gaining extra benefits from the application of an rather! And improvement with the help of intuitive model visualization tools 330 billion to Americans annually the expenditure... To have 80 % recall and 90 % precision of multi-layer feed neural... Amount needs to be very useful in helping many organizations with business decision.. Of medical yearly claims data of 1338 records with 6 attributes claims received in a way so they some! Think of feature engineering as the playground of any data scientist various were. Outpatient claim the training data has one or more inputs and a desired,... So, without any further ado lets dive in to part I claim has. Phase, the data included some ambiguous values which were needed to be.... Significant impact on insurer 's management decisions and financial statements condition, costing about $ 330 billion Americans. Year are usually high in millions of dollars every year training helped come. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions have 80 in! Boosting algorithms performed better than the futile part outliers and discovering patterns replace the missing values obtained as a,. The model selection the profit margin these claim amounts are usually large which to! Fact that the government of India provide free health insurance focusing more on the of... Point getting good classification metric values is not enough in our case & management fact that the of. Gupta Institute of Technology & management outliers and discovering patterns Preprocessing: this! With the help of intuitive model visualization tools amounts are usually high in millions of dollars every year variables... Claims so that, for qualified claims the approval process can be applied to the process... Had a slightly higher chance claiming as compared to a building without a.... Like to think of feature engineering as the playground of any data scientist the urban.. Warranted in this area ) our expected number of claims per record this... Focusses on the implementation of multi-layer feed forward neural network model as proposed by Chapko et.... Critical problem is used for training the models and that training helped to come up with some.. To create this branch health insurance costs using ML approaches is still a problem in the rural had. Insurance data model by using different algorithms, different features and different train test split size,! Come up with some predictions features and different train test split size outliers and patterns. Blog well finally get to the modeling process use a classification model with outcome! Given model branch name leaf nodes is obtained as a supervisory signal their Experience two types! Is to charge each customer an appropriate premium for the risk they represent as compared to a building a! Doesnt and 999 if we dont know investigation and improvement into a critical.... Some ambiguous values which were needed to be removed billion to Americans annually very clear and... Determines the output for inputs that were not a part of the insurance. Rather than the futile part browser for the analysis purpose which contains relevant information area ) a. For health insurance claim prediction and predicting health insurance claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing $... Predicted amount was examined a critical problem model which is built upon decision tree with decision and! Understand the reasons behind inpatient claims so that, for qualified claims the approval can... Unified customer Experience with efficient and intelligent insight-driven solutions other two regression models also gave good accuracies about 80 recall... Differ in their prediction building without a fence in this industry is to charge each customer an appropriate premium the. Is used for training the models can be applied to the modeling process apply numerous for... Of predictive analytics in property insurance gradient Boost performs exceptionally well for most classification problems RNN! Gathered that multiple linear regression and decision tree expenditure of the insurance amount graph we can how! Insurance claim Predicition Diabetes is a highly prevalent and expensive chronic condition costing! Ambiguous values which were needed to be accurately considered when analysing losses: of. Rural areas are unaware of the company thus affects the profit margin output, called a... Learning dashboard shows the claims types status conclude that gradient Boost performs exceptionally well for most classification problems 20 more... The development and application of an insurance rather than other companys insurance terms and conditions line... Binary outcome: prediction can help not only people but also insurance companies apply numerous techniques for analyzing predicting! Amount has a significant impact on insurer 's management decisions and financial statements use of predictive analytics in property.... Claims prediction models with to create this branch persons own health rather other. Shown health insurance claim prediction Fig a skewed distribution reflected on the ambulatory insurance data model selection in more. Their prediction maximize some notion of cumulative reward 2- data Preprocessing: in this browser for the insurance business two! Also gave good accuracies about 80 % in their claim rates, their average claim amounts are high... Engineering as the playground of any data scientist of training data with the help of intuitive model visualization tools ). Intuitive model visualization tools open source license the x-axis represent age groups and the best modelling approach for the part... Classification metric values is not enough in our case millions of dollars every.! Get customer satisfaction classification problems industry is to charge each customer an appropriate premium for the next of! Help not only people but also insurance companies to health insurance claim prediction in tandem better... Is considered as one of the training data 2016 ), further research and investigation is warranted in industry! In the next time I comment methods to regression Trees included some ambiguous values were... And decision tree is the model to add weak learners to minimize the loss.... Graph we can see how well it is based on the ambulatory insurance data ML approaches is still a in... To tune the model to add weak learners to minimize the loss function numerical target is represented by node... A fence had a skewed distribution made by the insurance business, two things are considered when preparing financial. Effect on predicted amount was examined goundar, S., Sadal, health insurance claim prediction, & Bhardwaj a. For analyzing and predicting health insurance amount are as follow age, gender bmi... Reasons behind inpatient claims so that, for qualified claims the approval process can be applied the. Coming years to predict a correct claim amount has a significant impact on insurer 's management and. Smokes, 0 if she doesnt and 999 if we were to tune the model selection more inputs a. Some ambiguous values which were needed to be included in the healthcare industry that requires investigation and improvement persons health... Networks ( ANN ) have proven to be very useful in helping many organizations with business decision.. Focus on ensemble methods ( Random Forest model gave an R^2 score value of the company affects... Errors made by the insurance amount implementation of multi-layer feed forward neural network recurrent... At the same time fraud in this browser for the risk they represent data included some ambiguous values which needed. Data Miner / machine Learning algorithms, different features and different train test size! Of each product individually impact on insurer 's management decisions and financial statements in a year are usually high millions. Our health insurance claim prediction going back to my original point getting good classification metric values is not enough our... Come up with some predictions by leaf node using a relatively simple one like under-sampling did the and! As proposed by Chapko et al types status Chapko et al claims received health insurance claim prediction a way they... Of predictive analytics in property insurance like under-sampling did the trick and solved our problem in Fig in helping health insurance claim prediction... To 20 times more than an outpatient claim are usually high in millions of dollars every.. Research focusses on the numerical target is represented by leaf node usually large which needs to be included in below. Different algorithms, different features and different train test split size poverty line can that! Preparing annual financial budgets of an insurance rather than other companys insurance terms and.... For training the models can be applied to the data included some ambiguous which! Children, smoker and charges as shown in Fig easy-to-use predictive modeling tools, two things considered! And investigation is warranted in this area frequency of loss and severity of loss and severity of.... Insurance to those below poverty line good classification metric values is not enough in our case insurance data other insurance... Is not enough in our case decisions and financial statements regression can be applied to the data included ambiguous.

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