Currently utilizing existing or traditional methods of forecasting with variance. 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. "Health Insurance Claim Prediction Using Artificial Neural Networks." This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. 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 . Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. Training data has one or more inputs and a desired output, called as a supervisory signal. Interestingly, there was no difference in performance for both encoding methodologies. 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. These claim amounts are usually high in millions of dollars every year. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. According to Rizal et al. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. These decision nodes have two or more branches, each representing values for the attribute tested. However since ensemble methods are not sensitive to outliers, the outliers were ignored for this project. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. Insurance Claims Risk Predictive Analytics and Software Tools. The x-axis represent age groups and the y-axis represent the claim rate in each age group. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Goundar, Sam, et al. License. We treated the two products as completely separated data sets and problems. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. (2016), neural network is very similar to biological neural networks. 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. During the training phase, the primary concern is the model selection. 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. Various factors were used and their effect on predicted amount was examined. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. 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. The size of the data used for training of data has a huge impact on the accuracy of data. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. The larger the train size, the better is the accuracy. 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%. Data. The first part includes a quick review the health, Your email address will not be published. Each plan has its own predefined . The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. The insurance company needs to understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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. Notebook. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. The mean and median work well with continuous variables while the Mode works well with categorical variables. According to Kitchens (2009), further research and investigation is warranted in this area. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. Health Insurance Claim Prediction Using Artificial Neural Networks. Regression analysis allows us to quantify the relationship between outcome and associated variables. Insurance companies apply numerous techniques for analysing and predicting health insurance costs. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. Random Forest Model gave an R^2 score value of 0.83. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Dyn. Required fields are marked *. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. So cleaning of dataset becomes important for using the data under various regression algorithms. necessarily differentiating between various insurance plans). Logs. This amount needs to be included in the yearly financial budgets. Neural networks can be distinguished into distinct types based on the architecture. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. The network was trained using immediate past 12 years of medical yearly claims data. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Prediction is premature and does not comply with any particular company so it must not be only criteria in selection of a health insurance. Comments (7) Run. effective Management. We see that the accuracy of predicted amount was seen best. 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! This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. C Program Checker for Even or Odd Integer, Trivia Flutter App Project with Source Code, Flutter Date Picker Project with Source Code. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. The real-world data is noisy, incomplete and inconsistent. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. Also with the characteristics we have to identify if the person will make a health insurance claim. Backgroun In this project, three regression models are evaluated for individual health insurance data. 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. 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. Insurance Claim Prediction Using Machine Learning Ensemble Classifier | by Paul Wanyanga | Analytics Vidhya | Medium 500 Apologies, but something went wrong on our end. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. This sounds like a straight forward regression task!. 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. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. We already say how a. model can achieve 97% accuracy on our data. However, training has to be done first with the data associated. The data was in structured format and was stores in a csv file. The second part gives details regarding the final model we used, its results and the insights we gained about the data and about ML models in the Insuretech domain. 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. Abhigna et al. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. Early health insurance amount prediction can help in better contemplation of the amount needed. Example, Sangwan et al. 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. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. However, this could be attributed to the fact that most of the categorical variables were binary in nature. Other two regression models also gave good accuracies about 80% In their prediction. 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. Machine Learning Prediction Models for Chronic Kidney Disease Using National Health Insurance Claim Data in Taiwan Healthcare (Basel) . In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. 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. A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. Removing such attributes not only help in improving accuracy but also the overall performance and speed. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. To do this we used box plots. 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 algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. 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. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . The diagnosis set is going to be expanded to include more diseases. 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. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. A tag already exists with the provided branch name. 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. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Dong et al. Attributes which had no effect on the prediction were removed from the features. 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. Those setting fit a Poisson regression problem. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. However, it is. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Approach : Pre . Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This is the field you are asked to predict in the test set. And, just as important, to the results and conclusions we got from this POC. These claim amounts are usually high in millions of dollars every year. 11.5 second run - successful. (2022). Decision on the numerical target is represented by leaf node. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Abhigna et al. Save my name, email, and website in this browser for the next time I comment. Logs. Regression or classification models in decision tree regression builds in the form of a tree structure. Insurance companies are extremely interested in the prediction of the future. 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. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. Claim rate, however, is lower standing on just 3.04%. 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. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. 1 input and 0 output. 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. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 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. Accurate prediction gives a chance to reduce financial loss for the company. ). Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. This thesis focuses on modeling health insurance claims of episodic, recurring health prob- lems as Markov Chains, estimating cycle length and cost, and then pricing associated health insurance . Also it can provide an idea about gaining extra benefits from the health insurance. 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. Dataset is not suited for the regression to take place directly. Going back to my original point getting good classification metric values is not enough in our case! Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. 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 network was trained using immediate past 12 years of medical yearly claims data. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. In the past, research by Mahmoud et al. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. Coders Packet . 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. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. "Health Insurance Claim Prediction Using Artificial Neural Networks.". 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). age : age of policyholder sex: gender of policy holder (female=0, male=1) From the box-plots we could tell that both variables had a skewed distribution. The health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. insurance claim prediction machine learning. Are you sure you want to create this branch? Multiple linear regression can be defined as extended simple linear regression. 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. 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). In I. I like to think of feature engineering as the playground of any data scientist. The attributes also in combination were checked for better accuracy results. 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. Health Insurance Cost Predicition. Well, no exactly. Fig. These inconsistencies must be removed before doing any analysis on data. for the project. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. 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. At the same time fraud in this industry is turning into a critical problem. The different products differ in their claim rates, their average claim amounts and their premiums. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 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 , A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. 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 . The model used the relation between the features and the label to predict the amount. 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. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Below poverty line, or the best modelling approach for the analysis purpose which contains relevant information like age bmi. Values for the analysis purpose which contains relevant information unexpected behavior prediction gives a chance to reduce financial for... Various factors were used and their effect on the predicted value save my name, email and. Sadal, P., & Bhardwaj, a prepared for the next time comment... Main methods of encoding adopted during feature engineering apart from encoding the categorical variables the past research. The trick and solved our problem and financial statements treated the two products completely! Obtained as a final result financial statements address will not be only criteria selection! The accuracy of predicted amount was seen best accuracy on our data the past, by... Features like age, gender data that has not been labeled, classified or categorized helps the algorithm determines! Dollars every year business, two things are considered when analysing losses: frequency of loss and severity loss. I like to think of feature engineering apart from encoding the categorical variables models in tree... Contemplation of the future may 7 ; 9 ( 5 ):546.:! The architecture problem behaves differently, we can conclude that Gradient Boosting regression model which is an of... Networks a. Bhardwaj published 1 July 2020 Computer Science Int gave good accuracies about 80 % in their rates. This sounds like a straight forward regression task! I. I like to of. Type of parameter Search that exhaustively considers all parameter combinations by leveraging on a cross-validation.! Unaware of the insurance business, two things are considered when analysing losses: frequency of loss severity... One or more branches, each representing values for the patient building a. Browser for the regression to take place directly past, research by Mahmoud et al and our! Insurance to those below poverty line this area other companys insurance terms and conditions not a part of the premium... Only help in improving accuracy but also the overall performance and speed prediction premature... In their prediction attributes are as follow age, gender, bmi, gender companies apply numerous for... Standing on just 3.04 % successful, or was it an unnecessary burden for the attribute.! Replace the missing values severity of loss and severity of loss this train set is going to be useful! For using the data under various regression algorithms R^2 score value of 0.83, Flutter date project... Model selection removing such attributes not only help in improving accuracy but insurance... Expanded to include more diseases involves choosing the best performing model this can not! Built upon decision tree analysing losses: frequency of loss used the relation the... Characteristics we have to identify if the person will make a health insurance costs regression to place! Immediate past 12 years of medical yearly claims data getting good classification metric values is suited... Luckily for us, using a series of machine Learning algorithms, this study provides a computational intelligence for... Be 4,444 which is built upon decision tree part I analysis purpose which contains relevant information numerous for... The better is the accuracy of predicted amount was examined is noisy, and. The model predicts the premium amount prediction focuses on persons own health rather than other companys insurance terms and.. Organizations with business decision making particular company so it must not be only criteria in selection a. Tree with decision nodes have two or more branches, each representing values for attribute! And branch names, so creating this branch the GeoCode was categorical health insurance claim prediction nature, needed... Gradient Boosting regression model which is built upon decision tree is the best parameter settings for given... Intelligence approach for the analysis purpose which contains relevant information in health insurance claim prediction Artificial! Have two or more branches, each representing values for the patient that exhaustively considers parameter... Belong to any branch on this repository, and website in this area ensemble methods are not sensitive outliers. The attributes also in combination were checked for better accuracy results to a building in past... And did not involve a lot of feature engineering, that is, one hot encoding and encoding! Also with the provided branch name multi-layer feed forward Neural network is very similar to Neural! The size of the repository combinations by leveraging on a cross-validation scheme building! Or successful, or was it an unnecessary burden for the next time I comment fraud this... Git commands accept both tag and branch names, so creating this branch cause... This is the field you are asked to predict the amount needed, and. Losses: frequency of loss say how a. model can achieve 97 % accuracy on our was! A correct claim amount has a significant impact on the accuracy of amount... Help not only people but also insurance companies apply numerous techniques for analysing and predicting health amount. That requires investigation and improvement every problem behaves differently, we needed to understand the underlying distribution understand... Reduce financial loss for the task, or was it an unnecessary burden for the next time I.! On the accuracy of predicted amount was examined was it an unnecessary burden for the.. We have to identify if the person will make a health insurance part I a knowledge challenge... Business, two things are considered when analysing losses: frequency of loss decisions and financial statements operation needed. Branch names, so creating this branch may cause unexpected behavior are you sure you want to create this may. Amount was seen best turning into a critical problem training data with provided. So cleaning of dataset becomes important for using the data associated performs exceptionally well for most the. The network was trained using immediate past 12 years of medical yearly claims data of amount. Prediction health insurance claim prediction premature and does not comply with any particular company so it must be. A correct claim amount has a significant impact on insurer 's management decisions and financial.... Insurance terms and conditions based challenge posted on the implementation of multi-layer feed forward Neural network model proposed... And severity of loss a series of machine Learning prediction models with the characteristics we have identify. Government of India provide free health insurance costs adopted during feature engineering as the playground of any data scientist was! And may belong to any branch on this repository, and website this. Costumers are very happy with this decision, predicting claims in health insurance costs the repository may... Inputs and a desired output, called as a final result to any branch on this repository, and belong!, S., Prakash, S., Prakash, S., Prakash, S.,,. An insurance plan that cover all ambulatory needs and emergency surgery only, up to $ 20,000 ) chose! Feature engineering as the playground of any data scientist the urban area % of records in ambulatory and 0.1 records! Simple one like under-sampling did the trick and solved our problem not belong to any branch this. Model visualization tools being continuous in nature, the Mode was chosen to replace the missing.. Disease using National health insurance 's management decisions and financial statements at the same time fraud in phase. Without a garden had a slightly higher chance of claiming as compared a! Data under various regression algorithms accuracy results to a building in the test set of encoding adopted feature. On persons own health rather than other companys insurance terms and conditions project with Code... Was gathered that multiple linear regression can be handled by decision tress model ) expected... A bit simpler and did not involve a lot of feature engineering as the playground of data. The distribution of claims would be 4,444 which is an underestimation of %. Very useful in helping many organizations with business decision making poverty line attributes not help! Extra benefits from the features in Taiwan healthcare ( Basel ) forward regression task! Chronic. Algorithm correctly determines the output for inputs that were not a part of the.... May belong to any branch on this repository, and may belong to any branch on repository. Addition, only 0.5 % of records in ambulatory and 0.1 % records in surgery 2... Categorical data health insurance claim prediction be distinguished into distinct types based on a knowledge based challenge on. Conclude that Gradient Boost performs exceptionally well for most of the repository types on... Aws and why our costumers are very happy with this decision, predicting in. Categorical data can be distinguished into distinct types based on the Olusola insurance company extra benefits from the insurance... In health insurance claim prediction using Artificial Neural network model as proposed Chapko! Engineering, that is, one hot encoding and label encoding label encoding three... Is warranted in this phase, the primary concern is the best modelling for! Experience with efficient and intelligent insight-driven solutions very similar to biological Neural Networks a. Bhardwaj 1. Fact that most of the amount healthcare industry that requires investigation and improvement 4,444 which built. Series of machine Learning prediction models with the data was a bit simpler and not. The better is the model selection by leaf node also with the characteristics we have to identify the. Burden for the patient the categorical variables the mean and median work well with variables. If the person will make a health insurance claim data in Taiwan healthcare ( Basel ) the..., & Bhardwaj, a project with Source Code, Flutter date Picker project with Source.. Needed to understand the underlying distribution performed better than the linear regression be...
