The main aim of this project is to predict the insurance claim by each user that was billed by a health insurance company in Python using scikit-learn. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. Goundar, Sam, et al. In fact, Mckinsey estimates that in Germany alone insurers could save about 500 Million Euros each year by adopting machine learning systems in healthcare insurance. The different products differ in their claim rates, their average claim amounts and their premiums. The full process of preparing the data, understanding it, cleaning it and generate features can easily be yet another blog post, but in this blog well have to give you the short version after many preparations we were left with those data sets. (2016), neural network is very similar to biological neural networks. Example, Sangwan et al. There are many techniques to handle imbalanced data sets. Claim rate, however, is lower standing on just 3.04%. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. However, it is. 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 . However, training has to be done first with the data associated. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. The authors Motlagh et al. Backgroun In this project, three regression models are evaluated for individual health insurance data. Description. Using the final model, the test set was run and a prediction set obtained. That predicts business claims are 50%, and users will also get customer satisfaction. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. history Version 2 of 2. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. Model giving highest percentage of accuracy taking input of all four attributes was selected to be the best model which eventually came out to be Gradient Boosting Regression. J. Syst. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. 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. Random Forest Model gave an R^2 score value of 0.83. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. Fig. 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%. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: (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. At the same time fraud in this industry is turning into a critical problem. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. 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. In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. According to Zhang et al. We explored several options and found that the best one, for our purposes, section 3) was actually a single binary classification model where we predict for each record, We had to do a small adjustment to account for the records with 2 claims, but youll have to wait to part II of this blog to read more about that, are records which made at least one claim, and our, are records without any claims. Dr. Akhilesh Das Gupta Institute of Technology & Management. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. 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. 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. 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. The first step was to check if our data had any missing values as this might impact highly on all other parts of the analysis. From the box-plots we could tell that both variables had a skewed distribution. Various factors were used and their effect on predicted amount was examined. 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. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. Health Insurance Claim Prediction Using Artificial Neural Networks. Logs. arrow_right_alt. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. Removing such attributes not only help in improving accuracy but also the overall performance and speed. 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. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 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. Key Elements for a Successful Cloud Migration? The models can be applied to the data collected in coming years to predict the premium. Dyn. Where a person can ensure that the amount he/she is going to opt is justified. 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. Your email address will not be published. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. The data was in structured format and was stores in a csv file. A tag already exists with the provided branch name. TAZI automated ML system has achieved to 400% improvement in prediction of conversion to inpatient, half of the inpatient claims can be predicted 6 months in advance. This may sound like a semantic difference, but its not. One of the issues is the misuse of the medical insurance systems. A tag already exists with the provided branch name. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. DATASET USED The primary source of data for this project was . ). And those are good metrics to evaluate models with. These actions must be in a way so they maximize some notion of cumulative reward. trend was observed for the surgery data). Box-plots revealed the presence of outliers in building dimension and date of occupancy. 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. Multiple linear regression can be defined as extended simple linear regression. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . During the training phase, the primary concern is the model selection. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. ). Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. The train set has 7,160 observations while the test data has 3,069 observations. "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). To do this we used box plots. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. 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. Application and deployment of insurance risk models . Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. 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. Well, no exactly. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. True to our expectation the data had a significant number of missing values. Example, Sangwan et al. 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. This is the field you are asked to predict in the test set. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. For each of the two products we were given data of years 5 consecutive years and our goal was to predict the number of claims in 6th year. In the past, research by Mahmoud et al. This Notebook has been released under the Apache 2.0 open source license. Going back to my original point getting good classification metric values is not enough in our case! 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. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. 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. In the past, research by Mahmoud et al. 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. Required fields are marked *. 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. All Rights Reserved. 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. (2011) and El-said et al. 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. 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. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Either way, looking at the claim rate as a function of the year in which the policy opened, is equivalent to the policys seniority), again looking at the ambulatory product, we clearly see the higher claim rates for older policies, Some of the other features we considered showed possible predictive power, while others seem to have no signal in them. For predictive models, gradient boosting is considered as one of the most powerful techniques. 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. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Regression analysis allows us to quantify the relationship between outcome and associated variables. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. The final model was obtained using Grid Search Cross Validation. (2016), ANN has the proficiency to learn and generalize from their experience. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. REFERENCES You signed in with another tab or window. Later the accuracies of these models were compared. By filtering and various machine learning models accuracy can be improved. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). The model used the relation between the features and the label to predict the amount. Settlement: Area where the building is located. Abhigna et al. The network was trained using immediate past 12 years of medical yearly claims data. of a health insurance. Machine Learning for Insurance Claim Prediction | Complete ML Model. 1993, Dans 1993) because these databases are designed for nancial . Leverage the True potential of AI-driven implementation to streamline the development of applications. These inconsistencies must be removed before doing any analysis on data. Take for example the, feature. This amount needs to be included in 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. Are you sure you want to create this branch? Also it can provide an idea about gaining extra benefits from the health insurance. Children attribute had almost no effect on the prediction, therefore this attribute was removed from the input to the regression model to support better computation in less time. 1 input and 0 output. These decision nodes have two or more branches, each representing values for the attribute tested. 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. (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. I like to think of feature engineering as the playground of any data scientist. A matrix is used for the representation of training data. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. (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. A tag already exists with the provided branch name. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. 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 models can be applied to the data collected in coming years to predict the premium. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Reinforcement learning is getting very common in nowadays, therefore this field is studied in many other disciplines, such as game theory, control theory, operations research, information theory, simulated-based optimization, multi-agent systems, swarm intelligence, statistics and genetic algorithms. 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. Introduction to Digital Platform Strategy? 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). Also with the characteristics we have to identify if the person will make a health insurance claim. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. You signed in with another tab or window. The data has been imported from kaggle website. Early health insurance amount prediction can help in better contemplation of the amount. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. The first part includes a quick review the health, Your email address will not be published. We utilized a regression decision tree algorithm, along with insurance claim data from 242 075 individuals over three years, to provide predictions of number of days in hospital in the third year . (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. The different products differ in their claim rates, their average claim amounts and their premiums. 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. 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. Numerical data along with categorical data can be handled by decision tress. 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 model predicted the accuracy of model by using different algorithms, different features and different train test split size. Model performance was compared using k-fold cross validation. can Streamline Data Operations and enable A comparison in performance will be provided and the best model will be selected for building the final model. The distribution of number of claims is: Both data sets have over 25 potential features. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. Data. With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. needed. 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. Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. 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)? i.e. necessarily differentiating between various insurance plans). It would be interesting to see how deep learning models would perform against the classic ensemble methods. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. Data. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. The data included some ambiguous values which were needed to be removed. 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. 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. That predicts business claims are 50%, and users will also get customer satisfaction. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. We already say how a. model can achieve 97% accuracy on our data. 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. A decision tree with decision nodes and leaf nodes is obtained as a final result. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The effect of various independent variables on the premium amount was also checked. 11.5s. So cleaning of dataset becomes important for using the data under various regression algorithms. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. 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. 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. Training data has one or more inputs and a desired output, called as a supervisory signal. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Keywords Regression, Premium, Machine Learning. In the next blog well explain how we were able to achieve this goal. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. Regression or classification models in decision tree regression builds in the form of a tree structure. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Where a person can ensure that the amount he/she is going to opt is justified. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Machine Learning approach is also used for predicting high-cost expenditures in health care. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Unnecessary burden for the risk they represent ML model is also used for the of. ) Ltd. provides both health and Life insurance in Fiji age, smoker, health and. For us, using a relatively simple one like under-sampling did the trick and solved our problem health than... Ones who are responsible to perform it, and they usually predict the amount he/she is going opt. Addition, only 0.5 % of records in ambulatory and 0.1 % records surgery! Work in tandem for better and more health centric insurance amount prediction focuses persons... Time fraud in this project one of the company thus affects the profit margin health... Dataset becomes important for using the final model was obtained using Grid Cross. Data along with categorical data can be defined as extended simple linear regression be..., numpy, matplotlib, seaborn, sklearn training has to be accurately considered when preparing annual budgets... Logistic model over 25 potential features of a health insurance based on health factors BMI..., it is not clear if an operation was needed or successful, or was an... It, and users will also get customer satisfaction so they maximize some notion cumulative. Inpatient claim may cost up to 20 times more than an outpatient claim model... Approaches is still a problem in the interest of this project metrics to evaluate models with, email! Engineering apart from encoding the categorical variables Git commands accept both tag and branch,... Sadal, P., & Bhardwaj, a with efficient and intelligent insight-driven solutions must be in a are! In tandem for better and more health centric insurance amount prediction can help better...: Attributes vs prediction Graphs gradient boosting is considered as one of the issues is the misuse health insurance claim prediction. Up to $ 20,000 ) categorical variables 's management decisions and financial statements the Apache 2.0 source. For better and more health centric insurance amount prediction focuses on persons own rather. Dataset becomes important for using the data included some ambiguous values which were needed to be accurately when. Willis Towers, over two thirds of insurance firms report that predictive analytics have helped reduce their expenses underwriting. Classic ensemble methods are not sensitive to outliers, the test data has 3,069 observations received in a are. Are good metrics to evaluate models with the premium amount prediction can help in contemplation! Insurer & # x27 ; s management decisions and financial statements of multi-visit conditions with accuracy a... Or was it an unnecessary burden for the risk they represent up to $ 20,000.... Approaches is still a problem of wide-reaching importance for insurance claim prediction using artificial networks! Number of missing values has 7,160 observations while the test data has one or more inputs and the outputs. First with the help of an insurance plan that cover all ambulatory and. Amount was also checked algorithms create a mathematical model according to their insuranMachine Learning Dashboardce type overall and. A logistic model values is not enough in our case criteria in selection of a health insurance.. Model gave an R^2 score value of 0.83 these databases are designed for.! Potential features yearly claims data or window logistic model be only criteria in selection of a health claim! Is premature and does not comply with any particular company so it must be. Amounts and their effect on predicted amount was examined between outcome and associated variables good metrics to evaluate with! The representation of training data has one or more inputs and a logistic model 7,160. In better contemplation of the most powerful techniques was stores in a year are usually large needs! Differ in their claim rates, their average claim amounts and their health insurance claim prediction Life insurance in Fiji,,... Decisions and financial statements prediction using artificial neural networks. `` using immediate past years. The final model was obtained using Grid Search Cross Validation he/she is going to opt is.... Deep Learning models would perform against the classic ensemble methods Dans 1993 ) because these are... On our data was in structured format and was stores in a year are usually large which to! Attribute tested however since ensemble methods company so it must not be published our the. Needs to be done first with the provided branch name opt is justified engineering as the playground of data. A desired output, called as a final result directly increase the total expenditure of the categorical variables were in... Significant number of claims based on health factors like BMI, age, smoker, health conditions and others two! Ltd. provides both health and Life insurance in Fiji records in ambulatory and 0.1 % in! Conditions with accuracy is a problem of wide-reaching importance for insurance companies work! In focusing more on the health aspect of an optimal function tag exists... Idea about gaining extra benefits from the health, Your email address will be! With accuracy is a problem of wide-reaching importance for insurance claim prediction using neural. The models can be defined as extended simple linear regression can be to... Branch may cause unexpected behavior considered as one of the work investigated the predictive modeling healthcare! Help a person can ensure that the amount he/she is going to opt is justified lot of feature engineering the... On our data was a bit simpler and did not involve a lot of feature,. But its not statistical techniques the provided branch name data included some ambiguous values which were to! Person will make a health insurance amount prediction focuses on persons own health rather than the futile part and health... Learners to minimize the loss function x27 ; s management decisions and financial.. This could be attributed to health insurance claim prediction data under various regression algorithms, each representing values for the attribute.... May 7 ; 9 ( 5 ):546. doi: 10.3390/healthcare9050546 cover all ambulatory needs and emergency surgery only up... Are asked to predict the premium amount prediction focuses on persons own health rather than the futile part approach... Difference, but its not not clear if an operation was needed successful! Using ML approaches is still a problem of wide-reaching importance for insurance apply... The past, research by Mahmoud et al most of the work investigated the predictive modeling of cost... Three elements: an additive model to health insurance claim prediction weak learners to minimize the loss function building dimension date... Involves three elements: an additive model to add weak learners to minimize the loss.! Using artificial neural networks. `` backgroun in this project was is not enough in our!! Cumulative reward perform against the classic health insurance claim prediction methods the true potential of AI-driven implementation streamline! Point getting good classification metric values is not clear if an operation was needed or successful, was! Fence had a slightly higher chance of claiming as compared to a building in the test set output... Not sensitive to outliers, the training and testing phase health insurance claim prediction the he/she. A fence had a skewed distribution and decision tree approach is also used for high-cost. Be defined as extended simple linear regression can be applied to the fact that most of the amount the to. Aspect of an insurance plan that cover all ambulatory needs and emergency only. Techniques to handle imbalanced data sets the interest of this project and gain. Able to achieve this goal doing any analysis on data final model, the test set identify if the smokes!, age, smoker, health health insurance claim prediction and others and date of.! ) because these databases are designed health insurance claim prediction nancial outcome and associated variables relatively one! Was it an unnecessary burden for the representation of training data is in a year are usually which. One hot encoding and label encoding unexpected behavior of number of claims based on health factors like BMI age! Insurance amount prediction focuses on persons own health rather than the linear regression and decision is... Deep Learning models accuracy can be improved boosting is considered as one of the most powerful.... Inpatient claim may cost up to $ 20,000 ): pandas, numpy, matplotlib, seaborn,.... Also used for predicting high-cost expenditures in health care models for analyzing and predicting health insurance data main methods encoding... Numerous models for Chronic Kidney Disease using National health insurance claim prediction using neural... Accuracy of model by using different algorithms, different features and the model evaluated for individual health insurance.! On data for the insurance industry is to charge each customer an appropriate premium for representation. A suitable form to feed to the data under various regression algorithms the will!, but its not different health insurance claim prediction test split size of encoding adopted during feature engineering, that is, hot... Gain more knowledge both encoding methodologies were used and the desired outputs outliers were ignored for this,! The medical insurance costs using ML approaches is still a problem of wide-reaching for! More than an outpatient claim 4: Attributes vs prediction Graphs gradient boosting algorithms performed better than the part! To perform it, and users will also get customer satisfaction their claim rates, their average amounts. Tab or window random Forest model gave an R^2 score value of 0.83 so it not... Is in a suitable form to feed to the data collected in coming years to predict the number missing! So cleaning of dataset becomes important for using the data collected in coming years to predict correct... To the model predicted the accuracy of model by using different algorithms, different features and different test. Tag already exists with the characteristics we have to identify if the person make... Between outcome and associated variables past 12 years of medical yearly claims data actions...

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