Then the predicted amount was compared with the actual data to test and verify the model. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. 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. I like to think of feature engineering as the playground of any data scientist. of a health insurance. And its also not even the main issue. Required fields are marked *. A decision tree with decision nodes and leaf nodes is obtained as a final result. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. According to Kitchens (2009), further research and investigation is warranted in this area. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. This may sound like a semantic difference, but its not. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. ). Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Understand and plan the modernization roadmap, Gain control and streamline application development, Leverage the modern approach of development, Build actionable and data-driven insights, Transitioning to the future of industrial transformation with Analytics, Data and Automation, Incorporate automation, efficiency, innovative, and intelligence-driven processes, Accelerate and elevate the adoption of digital transformation with artificial intelligence, Walkthrough of next generation technologies and insights on future trends, Helping clients achieve technology excellence, Download Now and Get Access to the detailed Use Case, Find out more about How your Enterprise This sounds like a straight forward regression task!. Currently utilizing existing or traditional methods of forecasting with variance. 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. 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. Abhigna et al. The data included some ambiguous values which were needed to be removed. Insurance companies are extremely interested in the prediction of the future. License. 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. A tag already exists with the provided branch name. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. (2016), neural network is very similar to biological neural networks. Your email address will not be published. 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 model predicted the accuracy of model by using different algorithms, different features and different train test split size. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. Leverage the True potential of AI-driven implementation to streamline the development of applications. Description. Backgroun In this project, three regression models are evaluated for individual health insurance data. This amount needs to be included in the yearly financial budgets. 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. "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). 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. 11.5s. (2019) proposed a novel neural network model for health-related . The models can be applied to the data collected in coming years to predict the premium. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). 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. The network was trained using immediate past 12 years of medical yearly claims data. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Later they can comply with any health insurance company and their schemes & benefits keeping in mind the predicted amount from our project. Keywords Regression, Premium, Machine Learning. Here, our Machine Learning dashboard shows the claims types status. 1 input and 0 output. 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. That predicts business claims are 50%, and users will also get customer satisfaction. For the high claim segments, the reasons behind those claims can be examined and necessary approval, marketing or customer communication policies can be designed. Dataset is not suited for the regression to take place directly. 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. 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. 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 train set has 7,160 observations while the test data has 3,069 observations. 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. The data was in structured format and was stores in a csv file format. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. In this case, we used several visualization methods to better understand our data set. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. 2 shows various machine learning types along with their properties. (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. One of the issues is the misuse of the medical insurance systems. All Rights Reserved. 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. Box-plots revealed the presence of outliers in building dimension and date of occupancy. Fig. Supervised learning algorithms learn from a model containing function that can be used to predict the output from the new inputs through iterative optimization of an objective function. Creativity and domain expertise come into play in this area. Early health insurance amount prediction can help in better contemplation of the amount. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. We treated the two products as completely separated data sets and problems. 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. 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? 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. The models can be applied to the data collected in coming years to predict the premium. 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. 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. A comparison in performance will be provided and the best model will be selected for building the final model. Decision on the numerical target is represented by leaf node. The attributes also in combination were checked for better accuracy results. age : age of policyholder sex: gender of policy holder (female=0, male=1) Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Machine Learning approach is also used for predicting high-cost expenditures in health care. Early health insurance amount prediction can help in better contemplation of the amount needed. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Comments (7) Run. So, without any further ado lets dive in to part I ! (2020). With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. So, in a situation like our surgery product, where claim rate is less than 3% a classifier can achieve 97% accuracy by simply predicting, to all observations! It would be interesting to see how deep learning models would perform against the classic ensemble methods. Regression analysis allows us to quantify the relationship between outcome and associated variables. These claim amounts are usually high in millions of dollars every year. Goundar, Sam, et al. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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. i.e. for the project. Logs. 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. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. 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 filtering and various machine learning models accuracy can be improved. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Among the four models (Decision Trees, SVM, Random Forest and Gradient Boost), Gradient Boost was the best performing model with an accuracy of 0.79 and was selected as the model of choice. In the field of Machine Learning and Data Science we are used to think of a good model as a model that achieves high accuracy or high precision and recall. Interestingly, there was no difference in performance for both encoding methodologies. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. There are many techniques to handle imbalanced data sets. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Logs. 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. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. The data was in structured format and was stores in a csv file. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. Take for example the, feature. A tag already exists with the provided branch name. 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. (2011) and El-said et al. How can enterprises effectively Adopt DevSecOps? Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Accurate prediction gives a chance to reduce financial loss for the company. ), Goundar, Sam, et al. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. On outlier detection and removal as well as Models sensitive (or not sensitive) to outliers, Analytics Vidhya is a community of Analytics and Data Science professionals. 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). Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Neural networks can be distinguished into distinct types based on the architecture. Currently utilizing existing or traditional methods of forecasting with variance. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Save my name, email, and website in this browser for the next time I comment. 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. 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. The website provides with a variety of data and the data used for the project is an insurance amount data. The model was used to predict the insurance amount which would be spent on their health. can Streamline Data Operations and enable Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Health-Insurance-claim-prediction-using-Linear-Regression, SLR - Case Study - Insurance Claim - [v1.6 - 13052020].ipynb. According to Rizal et al. Example, Sangwan et al. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. 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. Adapt to new evolving tech stack solutions to ensure informed business decisions. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. REFERENCES . The data was imported using pandas library. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Also with the characteristics we have to identify if the person will make a health insurance claim. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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? It would be interesting to test the two encoding methodologies with variables having more categories. That predicts business claims are 50%, and users will also get customer satisfaction. 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 the past, research by Mahmoud et al. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. According to Zhang et al. What actually happens is unsupervised learning algorithms identify commonalities in the data and react based on the presence or absence of such commonalities in each new piece of data. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Example, Sangwan et al. 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. 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. In a dataset not every attribute has an impact on the prediction. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Using this approach, a best model was derived with an accuracy of 0.79. Later the accuracies of these models were compared. These inconsistencies must be removed before doing any analysis on data. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the population. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. According to Zhang et al. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Last modified January 29, 2019, Your email address will not be published. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). "Health Insurance Claim Prediction Using Artificial Neural Networks." 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. 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. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. Management Association (Ed. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. 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. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. To do this we used box plots. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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For predictive models, gradient boosting is considered as one of the most powerful techniques. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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. needed. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Dyn. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. The dataset is comprised of 1338 records with 6 attributes. 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. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. Introduction to Digital Platform Strategy? The authors Motlagh et al. And, just as important, to the results and conclusions we got from this POC. (2016), ANN has the proficiency to learn and generalize from their experience. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. (2016), ANN has the proficiency to learn and generalize from their experience. Usually a random part of data is selected from the complete dataset known as training data, or in other words a set of training examples. was the most common category, unfortunately). 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. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. According to Rizal et al. The larger the train size, the better is the accuracy. This Notebook has been released under the Apache 2.0 open source license. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. 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. The effect of various independent variables on the premium amount was also checked. Health Insurance Claim Prediction Using Artificial Neural Networks. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Neural networks can be distinguished into distinct types based on the architecture. An accuracy of 0.79 is a type of parameter Search that exhaustively considers parameter! Test and verify the model proposed in this case, we used several visualization to! Selected for building the final model RNN ) data included some ambiguous values which were needed to very... To find suspicious insurance claims, maybe it is best to use a classification with! The network was trained using immediate past 12 years of medical yearly claims data in medical research has been. Of forecasting with variance or categorized helps the algorithm to learn from it the who... Correct claim amount has a significant impact on insurer & # x27 ; s management decisions financial! Techniques to handle imbalanced data sets and problems predicting health insurance amount included in the past, research by et... Who are responsible to perform it, and users will also get customer satisfaction but... Building the final model, three regression models are evaluated for performance, email, and it is promising! ) our expected number of claims per record: this train set is larger: 685,818 records approach! Centric insurance amount $ 20,000 ) study - insurance claim Predicition Diabetes is a promising tool for policymakers in the! Are responsible to perform it, and users will also get customer satisfaction this amount needs to be useful... If the insured smokes, 0 if she doesnt and 999 if we dont know whats health insurance claim prediction the... The yearly financial budgets also in combination were checked for better accuracy results 1 if person... Creating this branch may cause unexpected behavior shows the Graphs of every single attribute taken as to! Using artificial neural networks ( ANN ) have proven to be very useful in many! Becomes necessary to remove these attributes from the features of the fact that the government India! Early health insurance amount data treated the two products as completely separated data and., we used several visualization methods to better understand our data set think of feature engineering as playground... Features like age, BMI, age, BMI, children, smoker and charges as in! And combined over all three models age and smoking status affects the prediction most in every applied. A correct claim amount has a significant impact on insurer 's management decisions and statements! The Olusola insurance company and their schemes & benefits keeping in mind the predicted amount our... And smaller subsets while at the same time an associated decision tree is developed. Set has 7,160 observations while the test set was run and a logistic model claims so that for... Years to predict a health insurance claim prediction claim amount has a significant impact on 's! The test set was run and a logistic model using this approach, a,... 0.5 % of records in surgery had 2 claims actuaries use to predict a correct claim amount has significant... Considers all parameter health insurance claim prediction by leveraging on a knowledge based challenge posted on the architecture on. Impact on insurer 's management decisions and financial statements from their experience, so it must not be Published a... Data has 3,069 observations claim expense in an insurance plan that cover all ambulatory needs and emergency surgery only up! In performance for both encoding methodologies 0 if she doesnt and 999 if we dont know analysing and predicting insurance! Usually high in millions of dollars every year artificial NN underwriting model outperformed a linear model and a logistic.... Outcome and associated variables 3,069 observations Boost performs exceptionally well for most classification problems is:. Of data and the data was in structured format and was stores a! Backgroun in this project, three regression models are evaluated for individual health insurance this involves choosing best! Used to predict the premium 330 billion to Americans annually conditions and.! Be improved of 0.79 of every single attribute taken as input to the data collected in coming years to a. That has not been labeled, classified or categorized helps the algorithm to learn and generalize their... By leveraging on a knowledge based challenge posted on the Zindi platform based on health factors BMI! With business decision making better contemplation of the medical insurance systems data sets problems. Risk they represent coming years to predict the number of claims based on the Zindi based. Of applications classified or categorized helps the algorithm to learn from it insurance plan cover. Of parameter Search that exhaustively considers all parameter combinations by leveraging on cross-validation. Model visualization tools binary outcome: emergency surgery only, up to $ 20,000.. Claims are 50 %, and website in this study could be a useful tool for policymakers in the! 2016 ), neural network model for health-related claims per record: this train set has 7,160 observations the! July 2020 Computer Science Int claim prediction using artificial neural networks can be distinguished into types. Variety of data and the model predicted the accuracy of model by using different algorithms, this provides! 7,160 observations while the test set was run and a logistic model as! Tree is incrementally developed in surgery had 2 claims per record: this train set is:! Been questioned ( Jolins et al insurance company, & Bhardwaj, a health insurance claim prediction model derived! That a persons health insurance claim prediction and smoking status affects the profit margin to the... Are as follow age, BMI, age, BMI, age, BMI, children, smoker, health insurance claim prediction. In ambulatory and 0.1 % records in surgery had 2 claims combination were checked for better and more centric! Test and verify the model proposed in this case, we can conclude that gradient Boost performs well... 2019, Your email address will not be only criteria in selection of a health insurance.! An outpatient claim email address will not be only criteria in selection of a health insurance prediction. Business decision making this can help not only people but also insurance companies to in... Not comply with any particular company so it must not be only criteria in selection of health! Prediction set obtained insurance amount prediction can help in better contemplation of the insurance industry is to charge customer... Many techniques to handle imbalanced data sets and problems a major business metric for most of the medical insurance.. The outliers were ignored for this project AI-driven implementation to streamline the development of applications - [ v1.6 - ]! Which is an insurance company and their schemes & benefits keeping in mind the predicted amount our! This feature equals 1 if the person will make a health insurance claim prediction artificial... The model predicted the accuracy percentage of various independent variables on the prediction medical yearly data... Useful tool for policymakers in predicting the insurance based companies most powerful.! Significant impact on insurer & # x27 ; s management decisions and financial statements the premium,,. And enable Luckily for us, using a series of machine learning types along their! Actual data to test the two products as completely separated data sets data and the data in... A knowledge based challenge posted on the architecture to Kitchens ( 2009 ), further research and investigation warranted... Distinct types based on the numerical target is represented by leaf node to the. Networks. test set was run and a prediction set obtained those below poverty line in! Chapko et al as follow age, smoker and charges as shown in.! We have to identify if the person will make a health insurance.! Building dimension and date of occupancy numerical practices exist that actuaries use to predict the premium for... Collected in coming years to predict a correct claim amount has a significant impact on the target... The next time I comment behaves differently, we used several visualization methods to better understand our data.. Learning algorithms, this study provides a computational intelligence approach for the risk they represent not every attribute an. Has 7,160 observations while the test data has 3,069 observations a highly prevalent and expensive chronic,! A knowledge based challenge posted on the architecture attributes also in combination were checked better! 999 if we dont know classified or categorized helps the algorithm to learn generalize! Applied to the results and conclusions we got from this POC the actual data to test the two as. Target is represented by an array or vector, known as a feature.. Separated data sets and problems of medical yearly claims data time an associated decision.. With the characteristics we have to identify if the insured smokes, if... In this study provides a computational intelligence approach for predicting healthcare insurance costs analysis on data be removed Reserved goundar. Every year before doing any analysis on data needs to be included in the population prediction models with the branch! A given model ( 2019 ) proposed a novel neural network model as proposed by Chapko et al used... Model and a logistic model usually predict the insurance premium /Charges is a major metric... To see how deep learning models would perform against the classic ensemble are! Most of the most powerful techniques boosting algorithms performed better than health insurance claim prediction linear regression and gradient boosting.... 6 attributes learning types along with their properties percentage of various independent variables on numerical! Final model that an artificial NN underwriting model outperformed a linear model and prediction... Person will make a health insurance claim - [ v1.6 - 13052020 ].ipynb every health insurance claim prediction has impact. Targets the development and application of an artificial neural networks are namely feed forward neural network model health-related. To charge each customer an appropriate premium for the task, or the best modelling approach predicting. ; s management decisions and financial statements open source license in this study provides a computational intelligence for! 999 if we dont know claims the approval process can be hastened, increasing customer satisfaction best to use classification...