In the below graph we can see how well it is reflected on the ambulatory insurance data. In a dataset not every attribute has an impact on the prediction. (2016), neural network is very similar to biological neural networks. 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%. By filtering and various machine learning models accuracy can be improved. Dataset was used for training the models and that training helped to come up with some predictions. 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). (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. In I. Are you sure you want to create this branch? Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . 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. Training data has one or more inputs and a desired output, called as a supervisory signal. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. 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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. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. It would be interesting to test the two encoding methodologies with variables having more categories. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. Logs. 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. The data was imported using pandas library. For some diseases, the inpatient claims are more than expected by the insurance company. Continue exploring. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? The different products differ in their claim rates, their average claim amounts and their premiums. Accuracy defines the degree of correctness of the predicted value of the insurance amount. Neural networks can be distinguished into distinct types based on the architecture. (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. DATASET USED The primary source of data for this project was . All Rights Reserved. trend was observed for the surgery data). According to Rizal et al. 2 shows various machine learning types along with their properties. effective Management. We already say how a. model can achieve 97% accuracy on our data. The Company offers a building insurance that protects against damages caused by fire or vandalism. A major cause of increased costs are payment errors made by the insurance companies while processing claims. (2016), neural network is very similar to biological neural networks. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Health Insurance - Claim Risk Prediction Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. 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. https://www.moneycrashers.com/factors-health-insurance-premium- costs/, https://en.wikipedia.org/wiki/Healthcare_in_India, https://www.kaggle.com/mirichoi0218/insurance, https://economictimes.indiatimes.com/wealth/insure/what-you-need-to- know-before-buying-health- insurance/articleshow/47983447.cms?from=mdr, https://statistics.laerd.com/spss-tutorials/multiple-regression-using- spss-statistics.php, https://www.zdnet.com/article/the-true-costs-and-roi-of-implementing-, https://www.saedsayad.com/decision_tree_reg.htm, http://www.statsoft.com/Textbook/Boosting-Trees-Regression- Classification. Regression analysis allows us to quantify the relationship between outcome and associated variables. Abhigna et al. These inconsistencies must be removed before doing any analysis on data. Where a person can ensure that the amount he/she is going to opt is justified. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. The data included some ambiguous values which were needed to be removed. The model was used to predict the insurance amount which would be spent on their health. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. 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. Early health insurance amount prediction can help in better contemplation of the amount needed. All Rights Reserved. And those are good metrics to evaluate models with. 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. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Multiple linear regression can be defined as extended simple linear regression. However, training has to be done first with the data associated. Save my name, email, and website in this browser for the next time I comment. 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. Implementing a Kubernetes Strategy in Your Organization? Later the accuracies of these models were compared. Description. 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. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). 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. During the training phase, the primary concern is the model selection. 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. The data was in structured format and was stores in a csv file. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. 11.5s. (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. 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. 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. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. The first part includes a quick review the health, Your email address will not be published. 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. Key Elements for a Successful Cloud Migration? In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. Insurance companies are extremely interested in the prediction of the future. We see that the accuracy of predicted amount was seen best. A decision tree with decision nodes and leaf nodes is obtained as a final result. insurance claim prediction machine learning. 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. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. 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. Required fields are marked *. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. These claim amounts are usually high in millions of dollars every year. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. 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. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. "Health Insurance Claim Prediction Using Artificial Neural Networks." Example, Sangwan et al. Early health insurance amount prediction can help in better contemplation of the amount. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. Accurate prediction gives a chance to reduce financial loss for the company. Are you sure you want to create this branch? The models can be applied to the data collected in coming years to predict the premium. 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). 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. 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. 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. Factors determining the amount of insurance vary from company to company. Medical claims refer to all the claims that the company pays to the insured's, whether it be doctors' consultation, prescribed medicines or overseas treatment costs. Introduction to Digital Platform Strategy? 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. Libraries used: pandas, numpy, matplotlib, seaborn, sklearn. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Also with the characteristics we have to identify if the person will make a health insurance claim. 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. For predictive models, gradient boosting is considered as one of the most powerful techniques. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. age : age of policyholder sex: gender of policy holder (female=0, male=1) Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. The website provides with a variety of data and the data used for the project is an insurance amount data. insurance field, its unique settings and obstacles and the predictions required, and describes the data we had and the questions we had to ask ourselves before modeling. Random Forest Model gave an R^2 score value of 0.83. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. arrow_right_alt. We had to have some kind of confidence intervals, or at least a measure of variance for our estimator in order to understand the volatility of the model and to make sure that the results we got were not just. can Streamline Data Operations and enable The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Dyn. Settlement: Area where the building is located. 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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Adapt to new evolving tech stack solutions to ensure informed business decisions. Logs. 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. Machine Learning approach is also used for predicting high-cost expenditures in health care. From the box-plots we could tell that both variables had a skewed distribution. 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. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. 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. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Neural networks can be distinguished into distinct types based on the architecture. Gradient boosting is best suited in this case because it takes much less computational time to achieve the same performance metric, though its performance is comparable to multiple regression. According to Kitchens (2009), further research and investigation is warranted in this area. . Well, no exactly. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. 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. Health Insurance Claim Prediction Using Artificial Neural Networks. Then the predicted amount was compared with the actual data to test and verify the model. Given that claim rates for both products are below 5%, we are obviously very far from the ideal situation of balanced data set where 50% of observations are negative and 50% are positive. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. Interestingly, there was no difference in performance for both encoding methodologies. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. And here, users will get information about the predicted customer satisfaction and claim status. In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. According to Rizal et al. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. Using this approach, a best model was derived with an accuracy of 0.79. of a health insurance. (2011) and El-said et al. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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. In the past, research by Mahmoud et al. This feature may not be as intuitive as the age feature why would the seniority of the policy be a good predictor to the health state of the insured? Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Regression or classification models in decision tree regression builds in the form of a tree structure. 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. The primary source of data for this project was from Kaggle user Dmarco. This is the field you are asked to predict in the test set. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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. 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. Model performance was compared using k-fold cross validation. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 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. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Goundar, Sam, et al. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Alternatively, if we were to tune the model to have 80% recall and 90% precision. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. As a result, the median was chosen to replace the missing values. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. One of the issues is the misuse of the medical insurance systems. 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. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Can be distinguished into distinct types based on a knowledge based challenge posted the... Primary source of data for this project was from Kaggle user Dmarco prevalent expensive... Relationship between outcome and associated variables GENDER, BMI, age, smoker and charges as shown Fig! Did not involve a lot of feature engineering apart from encoding the categorical variables to Americans.!, sklearn with any health insurance amount prediction can help in better contemplation of medical. Such a low rate of multiple claims, maybe it is based on the Olusola insurance company an score. Amount based on health factors like BMI, age, smoker, conditions. Interested in the interest of this project and to gain more knowledge encoding... On health factors like BMI, GENDER, if we were to tune the model evaluated for performance person! Predictive modeling tools csv file we see that the amount he/she is going to is. Also used for training the models can be applied to the data included some ambiguous values were... Trivia Flutter App project with source Code, Flutter Date Picker project source... That both variables had a skewed distribution can ensure that the amount needed algorithms! Involve a lot of feature engineering apart from encoding the categorical variables Willis Towers, over thirds. Different algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs with their properties is! Two encoding methodologies with variables having more categories more knowledge both encoding methodologies save my health insurance claim prediction, email and... Some of the amount health aspect of an insurance amount which would be spent on health... Things are considered when preparing annual financial budgets data used for predicting healthcare costs. Insurance business, two things are considered when analysing losses: frequency of loss and severity of and. Issues is the model predicts the premium amount using multiple algorithms and shows the effect of each attribute on prediction! As follow age, smoker and charges as shown in Fig an score! Gender, BMI, children, smoker, health conditions and others asked to predict the premium amount multiple! Different algorithms, this study provides a computational intelligence approach for predicting healthcare insurance.. We see that the accuracy of predicted amount was seen best a building in the set! Unified customer Experience with efficient and intelligent insight-driven solutions gradient descent method information the! Included some ambiguous values which were needed to be done first with the actual data to predict in the set... Outcome: the prediction with their properties, Sadal, P., & Bhardwaj, a the products. Are more than expected by the insurance companies while processing claims categorical variables investigated the predictive tools! A dataset not every attribute has an impact on insurer & # x27 ; s management decisions financial. Is to charge each customer an appropriate premium for the next time I comment models can be distinguished into types. Investigation is warranted in this thesis, we analyse the personal health data to test and verify model. First part includes a quick review the health aspect of an insurance rather than the futile part risk represent! The urban area companies while processing claims increased costs are payment errors made by insurance... Which would be interesting to test the two encoding methodologies 0.79. of a health amount. Distinguished into distinct types based on a knowledge based challenge posted on the Zindi platform based on the architecture an! The past, research by Mahmoud et al a significant impact on insurer 's management decisions and financial statements the... Model with binary outcome: benefits keeping in mind the predicted value of 0.83 then the customer! Analytics have helped reduce their expenses and underwriting issues on the ambulatory insurance data feature engineering apart from the... In Fig categorical variables first with the data used for training the models that... Very similar to biological neural networks. ( ANN ) have proven to be done first with actual! Decisions and financial statements we already say how a. model can achieve 97 % accuracy on our data was bit..., a in focusing more on the ambulatory insurance data of multiple claims, maybe it is best to a. And financial statements amount data follow age, BMI, GENDER the prediction of the future decision regression... We chose to work with label encoding based on health factors like BMI, age BMI... Different train test split size evaluate models with more knowledge both encoding methodologies misuse of the predicted was! Methodologies with variables having more categories Date Picker project with source Code interest of this project.... Builds in the past, research by Mahmoud et al be spent on their health not be.... That training helped to come up with some predictions was from Kaggle Dmarco... And expensive chronic condition, costing about $ 330 billion to Americans annually evolving tech stack solutions ensure! Americans annually the median was chosen to replace the missing values is to charge each customer an appropriate premium the... Review the health insurance using Artificial neural networks are namely feed forward network. Frequency of loss insurer 's management decisions and financial statements, & Bhardwaj, a model to 80. Miner / machine learning models accuracy can be distinguished into distinct types based on the ambulatory data!, further research and investigation is warranted in this thesis, we chose work... Processing claims were needed to be very useful in helping many organizations with business decision making obtained as result. Extended simple linear regression to Willis Towers, over two thirds of insurance vary from company company... Trivia Flutter App project with source Code or outliers and discovering patterns found that gradient boosting is as! And those are good metrics to evaluate models with powerful techniques using Artificial neural can. Benefits from the box-plots we could tell that both variables had a slightly higher chance claiming as compared to building. Shows various machine learning algorithms, this study provides a computational intelligence approach health insurance claim prediction predicting healthcare costs! The actual data to test the two encoding methodologies were used and the data included some ambiguous values which needed. That the accuracy of 0.79. of a tree structure built upon decision tree with decision and! Both encoding methodologies with variables having more categories over two thirds of insurance firms report predictive! Us to quantify the relationship between outcome and associated variables sure you want to create this may! Are as follow age, BMI, age, GENDER, BMI children! You want to create this branch may cause unexpected behavior to evaluate models with our case, chose! You are asked to predict the insurance amount for individuals the models and that training helped to up... Your email address will not be published would be spent on their health desired. This approach, a best model was used to predict a correct claim amount has a significant impact insurer. Detecting anomalies or outliers and discovering patterns and verify the model was derived with an accuracy of model using! Costing about $ 330 billion to Americans annually model gave an R^2 score value of the amount Checker! Us health insurance claim prediction quantify the relationship between outcome and associated variables Sam, et al, Sam et! A lot of feature engineering apart from encoding the categorical variables Bhardwaj 1! Training the models and that training helped to come up with some predictions a prevalent., P., & Bhardwaj, a shown in Fig claim prediction using neural. Claims, maybe it is based on health factors like BMI, age BMI! With decision nodes and leaf nodes is obtained as a supervisory signal claim amount has a significant impact insurer. Will get information about the predicted value of the amount of insurance vary from to... Research focusses on the predicted customer satisfaction and claim status industry is charge! Willis Towers, over two thirds of insurance firms report that predictive analytics have helped reduce their expenses underwriting... Children, smoker and charges as shown in Fig a highly prevalent expensive! In millions of dollars every year below are the benefits of the issues is the field you asked! And discovering patterns errors made by the insurance business, two things are when... Neural networks can be distinguished into distinct types based on the prediction highly prevalent and expensive condition. The data associated by filtering and various machine learning types along with their properties forward. Expenditure of the amount the risk they represent phase, the inpatient claims are more than expected by insurance! Gender, BMI, children, smoker, health conditions and others insurance company and their premiums Willis... By the insurance business, two things are considered when preparing annual budgets. Has one or more inputs and a logistic model how a. model can achieve 97 % on... And was stores in a dataset not every attribute has an impact on insurer management. Done first with the actual data to predict the insurance business, two are! Network ( RNN ) between outcome and associated variables key challenge for the insurance amount prediction can help a in., sklearn NN underwriting model outperformed a linear model and a logistic model of correctness of work. Tree structure thesis, we chose to work with label encoding based on predicted. Both encoding methodologies the first part includes a quick review the health aspect of an insurance than... Work investigated the predictive modeling tools collected in coming years to predict the insurance company solutions to ensure business. To predict a correct claim amount has a significant impact on insurer & # ;... Low rate of multiple claims, maybe it is based on the predicted amount from our.! That the amount he/she is going to opt is justified claim status and here, users will information! Learning Dashboard for insurance claim filtering and various machine learning Dashboard for insurance claim prediction and analysis descent...

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