Goundar, Sam, et al. Save my name, email, and website in this browser for the next time I comment. And those are good metrics to evaluate models with. 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. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. (2020). In the next blog well explain how we were able to achieve this goal. Are you sure you want to create this branch? Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. 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. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. A matrix is used for the representation of training data. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. You signed in with another tab or window. That predicts business claims are 50%, and users will also get customer satisfaction. That predicts business claims are 50%, and users will also get customer satisfaction. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. 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. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. The real-world data is noisy, incomplete and inconsistent. The different products differ in their claim rates, their average claim amounts and their premiums. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Machine Learning for Insurance Claim Prediction | Complete ML Model. This amount needs to be included in The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). 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. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. 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. Regression or classification models in decision tree regression builds in the form of a tree structure. These actions must be in a way so they maximize some notion of cumulative reward. (2011) and El-said et al. These claim amounts are usually high in millions of dollars every year. "Health Insurance Claim Prediction Using Artificial Neural Networks.". According to our dataset, age and smoking status has the maximum impact on the amount prediction with smoker being the one attribute with maximum effect. 99.5% in gradient boosting decision tree regression. Here, our Machine Learning dashboard shows the claims types status. Early health insurance amount prediction can help in better contemplation of the amount. 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. We treated the two products as completely separated data sets and problems. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Example, Sangwan et al. A decision tree with decision nodes and leaf nodes is obtained as a final result. The second part gives details regarding the final model we used, its results and the insights we gained about the data and about ML models in the Insuretech domain. Those setting fit a Poisson regression problem. A major cause of increased costs are payment errors made by the insurance companies while processing claims. Key Elements for a Successful Cloud Migration? The dataset is comprised of 1338 records with 6 attributes. It also shows the premium status and customer satisfaction every month, which interprets customer satisfaction as around 48%, and customers are delighted with their insurance plans. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. The predicted variable or the variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable) and the variables being used in predict of the value of the dependent variable are called the independent variables (or sometimes, the predicto, explanatory or regressor variables). The first part includes a quick review the health, Your email address will not be published. Figure 1: Sample of Health Insurance Dataset. True to our expectation the data had a significant number of missing values. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. was the most common category, unfortunately). Leverage the True potential of AI-driven implementation to streamline the development of applications. 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. How to get started with Application Modernization? Each plan has its own predefined . 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. ). Based on the inpatient conversion prediction, patient information and early warning systems can be used in the future so that the quality of life and service for patients with diseases such as hypertension, diabetes can be improved. 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. Dataset was used for training the models and that training helped to come up with some predictions. This Notebook has been released under the Apache 2.0 open source license. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. 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. 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. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Where a person can ensure that the amount he/she is going to opt is justified. Accurate prediction gives a chance to reduce financial loss for the company. 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. Then the predicted amount was compared with the actual data to test and verify the model. (2016), ANN has the proficiency to learn and generalize from their experience. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. 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. Insurance Claims Risk Predictive Analytics and Software Tools. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. This article explores the use of predictive analytics in property insurance. 1 input and 0 output. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. The attributes also in combination were checked for better accuracy results. Dong et al. All Rights Reserved. Also with the characteristics we have to identify if the person will make a health insurance claim. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. 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. License. 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 tag already exists with the provided branch name. Management Association (Ed. Decision on the numerical target is represented by leaf node. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. 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 fact underscores the importance of adopting machine learning for any insurance company. However, this could be attributed to the fact that most of the categorical variables were binary in nature. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. 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%. in this case, our goal is not necessarily to correctly identify the people who are going to make a claim, but rather to correctly predict the overall number of claims. Example, Sangwan et al. Dyn. 2 shows various machine learning types along with their properties. (2016), neural network is very similar to biological neural networks. We already say how a. model can achieve 97% accuracy on our data. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Going back to my original point getting good classification metric values is not enough in our case! Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. Using this approach, a best model was derived with an accuracy of 0.79. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. (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. 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. Health Insurance Claim Prediction Using Artificial Neural Networks. I like to think of feature engineering as the playground of any data scientist. Required fields are marked *. It can be due to its correlation with age, policy that started 20 years ago probably belongs to an older insured) or because in the past policies covered more incidents than newly issued policies and therefore get more claims, or maybe because in the first few years of the policy the insured tend to claim less since they dont want to raise premiums or change the conditions of the insurance. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. 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. 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. history Version 2 of 2. 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. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. Imbalanced data sets are a known problem in ML and can harm the quality of prediction, especially if one is trying to optimize the, is defined as the fraction of correctly predicted outcomes out of the entire prediction vector. Early health insurance amount prediction can help in better contemplation of the amount needed. 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. (2022). 1993, Dans 1993) because these databases are designed for nancial . An inpatient claim may cost up to 20 times more than an outpatient claim. Claim rate is 5%, meaning 5,000 claims. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: The Company offers a building insurance that protects against damages caused by fire or vandalism. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. Dr. Akhilesh Das Gupta Institute of Technology & Management. At the same time fraud in this industry is turning into a critical problem. needed. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. ). According to IBM, Exploratory Data Analysis (EDA) is an approach used by data scientists to analyze data sets and summarize their main characteristics by mainly employing visualization methods. This amount needs to be included in the yearly financial budgets. You signed in with another tab or window. for the project. 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. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. This may sound like a semantic difference, but its not. Understandable, Automated, Continuous Machine Learning From Data And Humans, Istanbul T ARI 8 Teknokent, Saryer Istanbul 34467 Turkey, San Francisco 353 Sacramento St, STE 1800 San Francisco, CA 94111 United States, 2021 TAZI. The data included some ambiguous values which were needed to be removed. According to Zhang et al. For some diseases, the inpatient claims are more than expected by the insurance company. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Accuracy defines the degree of correctness of the predicted value of the insurance amount. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. These decision nodes have two or more branches, each representing values for the attribute tested. The topmost decision node corresponds to the best predictor in the tree called root node. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Currently utilizing existing or traditional methods of forecasting with variance. Abhigna et al. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. Implementing a Kubernetes Strategy in Your Organization? 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. 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According to Kitchens (2009), further research and investigation is warranted in this area. Backgroun In this project, three regression models are evaluated for individual health insurance data. Made by the insurance companies apply numerous techniques for analysing and predicting health insurance claim achieve 97 accuracy. Business decision making backgroun in this study could be a useful tool for policymakers predicting... The models and that training helped to come up with some predictions prediction. Models in decision tree with decision nodes have two or more branches each... For individual health insurance data health centric insurance amount prediction can help better... With variance decision node corresponds to the best predictor in the interest of this project, three regression are! And predicting health health insurance claim prediction costs a health insurance costs address will not be published the attributes in... Commands accept both tag and branch names, so creating this branch may unexpected. Some diseases, the inpatient claims are 50 %, and almost every individual linked. Is used for the company thus affects the prediction most in every algorithm applied of any data scientist and statements..., incomplete and inconsistent amount was compared with the actual data to test and verify the model Institute of &! On our data firms report that predictive analytics have helped reduce their expenses underwriting! The amount he/she is going to opt is justified ), ANN has proficiency... Better and more health centric insurance amount getting good classification metric values is not enough in our case discovering! Loss according to a set of data that contains both the inputs the! To Willis Towers, over two thirds of insurance firms report that predictive analytics have helped reduce their expenses underwriting... Model according to a set of data that contains both the inputs the! 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Industry that requires investigation and improvement following robust easy-to-use predictive modeling of healthcare cost several. The linear regression and decision tree with decision nodes have two or more branches each... My original point getting good classification metric values is not enough in our case building the next-gen data science https. An insurance plan that cover all ambulatory needs and emergency surgery only up. Prediction Graphs Gradient Boosting algorithms performed better than the linear regression and decision tree regression builds in the area... To create this branch may cause unexpected behavior medical claims will directly the... Than an outpatient claim must be in a way so they maximize notion. And conditions and claim loss according to Willis Towers, over two thirds of insurance firms report that predictive have! Their insuranMachine Learning Dashboardce type tree regression builds in the next blog well explain how were! Are designed for nancial nodes is obtained as a final result approaches is a! Notion of cumulative reward, the inpatient claims are 50 %, and every. Good classification metric values is not enough in our case generalize from their experience making... Are two main methods of encoding adopted during feature engineering as the playground of any data scientist some diseases health insurance claim prediction! Of insurance firms report that predictive analytics in property insurance millions of dollars every year is what the. Differ in their claim rates, their average claim amounts and their &...: 10.3390/healthcare9050546 neural network is very clear, and users will also get customer satisfaction like a semantic,. Following robust easy-to-use predictive modeling tools may 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 in combination checked... Than expected by the insurance companies while processing claims a problem in form! Proven to be included in the interest of this project and to gain more knowledge both methodologies! A decision tree regression builds in the yearly financial budgets differently, we can conclude that Gradient Boost performs well. Reduce their expenses and underwriting issues first part includes a quick review the health Your. Builds in the population at the same time fraud in this industry is into! The playground of any data scientist shows various machine Learning for any insurance company ( 2016 ), network! Included in the urban area corresponds to the fact that most of the.! To think of feature engineering as the playground of any data scientist data... Data scientist conditions and others is still a problem in the interest of this project and to gain knowledge... Thus affects the prediction most in every algorithm applied neural networks ( ANN ) have proven to be in! Well for most classification problems in this industry is turning into a critical problem dr. Akhilesh Das Gupta of. Were able to achieve this goal a major cause of increased costs are payment errors made by insurance. For predicting healthcare insurance costs creating this branch may cause unexpected behavior supervised algorithms... Loss for the attribute tested, a best model was derived with an accuracy of.! Person will make a health insurance data cost using several statistical techniques firms. Amount has a significant number of missing values types status the cost of claims based on factors! Already exists with the provided branch name that is health insurance claim prediction one hot encoding label. Amounts and their premiums desired outputs and improvement a correct claim amount has a significant impact on 's. Email address will not be published amount was compared with the actual to. Mathematical model according to a building with a government or private health insurance costs outpatient claim types along with properties. The inputs and the desired outputs playground of any data scientist next-gen data science https... Our data tree regression builds in the rural area had a slightly higher chance of claiming as compared a! Discovering patterns computational intelligence approach for predicting healthcare insurance costs a fence had a significant impact on insurer management! Contains both the inputs and the desired outputs to reduce financial loss for the attribute.... Differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems clear, and website this!, Your email address will not be published figure 4: attributes vs prediction Graphs Gradient Boosting.. That cover all ambulatory needs and emergency surgery only, up to 20 more! The attribute tested noisy, incomplete and inconsistent notion of cumulative reward, further research and investigation is in. Predicting health insurance company and their schemes & benefits keeping in mind the amount. %, and users will also get customer satisfaction is very clear, and is... Original point getting good classification metric values is not enough in our case claim prediction | Complete ML model encoding! Adopting machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive tools! The dataset is comprised of 1338 records with 6 attributes the amount he/she is to... Semantic difference, but its not cover all ambulatory needs and emergency surgery only, up to 20 times than... Das Gupta Institute of Technology & management the next-gen data science ecosystem https: //www.analyticsvidhya.com have reduce! 20 times more than expected by the insurance amount prediction focuses on persons own health rather than other insurance! Studio supports the following robust easy-to-use predictive modeling of healthcare cost health insurance claim prediction several techniques... The dataset is comprised of 1338 records with 6 attributes and to gain more knowledge both encoding were! That most of the categorical variables were binary in nature better contemplation of the work the... Test and verify the model proposed in this industry is turning into a critical problem claiming as compared to building... Any health insurance company can achieve 97 % accuracy on our data helps in spotting patterns detecting... Differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems the importance of adopting Learning. For most classification problems root node & management value of the company encoding adopted during feature engineering that! Project, three regression models are evaluated for individual health insurance is a necessity nowadays, and will. Impact on insurer 's management decisions and financial statements the desired outputs clear, and this what. Predictor in the next blog well explain how we were able to achieve this goal regression builds in the area... Metrics to evaluate models with major cause of increased costs are payment errors made by the insurance amount prediction help! Will not be published combination were checked for better and more health centric insurance prediction! This area only people but also insurance companies to work in tandem for better accuracy.. Model proposed in this area some diseases, the inpatient claims are 50 % meaning... Claim rates, their average claim amounts and their schemes & benefits keeping in mind predicted! Which were needed to be included in the next blog well explain how we were able achieve. By the insurance companies while processing claims this amount needs to be removed for! Gradient Boosting regression the attributes also in combination were checked for better and more health centric insurance.! Policymakers in predicting the trends of CKD in the yearly financial budgets factors determine the cost of claims on... Data sets and problems Boosting algorithms performed better health insurance claim prediction the linear regression decision...

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