Implementing a Kubernetes Strategy in Your Organization? 1. 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. Claim rate is 5%, meaning 5,000 claims. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. effective Management. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. 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. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Accuracy defines the degree of correctness of the predicted value of the insurance amount. 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. (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. Are you sure you want to create this branch? Claim rate, however, is lower standing on just 3.04%. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: 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 . It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. In neural network forecasting, usually the results get very close to the true or actual values simply because this model can be iteratively be adjusted so that errors are reduced. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Pre-processing and cleaning of data are one of the most important tasks that must be one before dataset can be used for machine learning. At the same time fraud in this industry is turning into a critical problem. Training data has one or more inputs and a desired output, called as a supervisory signal. 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. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. This amount needs to be included in the yearly financial budgets. Data. And those are good metrics to evaluate models with. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. An inpatient claim may cost up to 20 times more than an outpatient claim. The dataset is comprised of 1338 records with 6 attributes. In the past, research by Mahmoud et al. 11.5s. 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. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. 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. Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. Predicting the Insurance premium /Charges is a major business metric for most of the Insurance based companies. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. 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. 1993, Dans 1993) because these databases are designed for nancial . You signed in with another tab or window. ). Adapt to new evolving tech stack solutions to ensure informed business decisions. The data was in structured format and was stores in a csv file. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. It would be interesting to see how deep learning models would perform against the classic ensemble methods. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. Model performance was compared using k-fold cross validation. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Copyright 1988-2023, IGI Global - All Rights Reserved, Goundar, Sam, et al. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Factors determining the amount of insurance vary from company to company. Random Forest Model gave an R^2 score value of 0.83. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. The topmost decision node corresponds to the best predictor in the tree called root node. Machine learning can be defined as the process of teaching a computer system which allows it to make accurate predictions after the data is fed. 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. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Dyn. 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. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. 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. 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. In I. 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 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. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. . According to Rizal et al. The attributes also in combination were checked for better accuracy results. 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? 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. You signed in with another tab or window. 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. Those setting fit a Poisson regression problem. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. arrow_right_alt. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Goundar, Sam, et al. The insurance user's historical data can get data from accessible sources like. 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 primary source of data for this project was from Kaggle user Dmarco. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. The mean and median work well with continuous variables while the Mode works well with categorical variables. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. 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. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. 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. During the training phase, the primary concern is the model selection. To do this we used box plots. 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 is very complex method and some rural people either buy some private health insurance or do not invest money in health insurance at all. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression 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. 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. So, without any further ado lets dive in to part I ! 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. Numerical data along with categorical data can be handled by decision tress. 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. for the project. The different products differ in their claim rates, their average claim amounts and their premiums. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. 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. Insurance Companies apply numerous models for analyzing and predicting health insurance cost. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. 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. Currently utilizing existing or traditional methods of forecasting with variance. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. Here, our Machine Learning dashboard shows the claims types status. 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. Why we chose AWS and why our costumers are very happy with this decision, Predicting claims in health insurance Part I. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. Machine Learning approach is also used for predicting high-cost expenditures in health care. These claim amounts are usually high in millions of dollars every year. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. The data was imported using pandas library. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). I like to think of feature engineering as the playground of any data scientist. Insurance Claim Prediction Problem Statement A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. "Health Insurance Claim Prediction Using Artificial Neural Networks." There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. 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. PREDICTING HEALTH INSURANCE AMOUNT BASED ON FEATURES LIKE AGE, BMI , GENDER . According to Kitchens (2009), further research and investigation is warranted in this area. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Users can quickly get the status of all the information about claims and satisfaction. Abhigna et al. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 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). Proven to be accurately considered when preparing annual financial budgets Bhardwaj, a product individually the yearly budgets! One or more inputs and a desired output, called as a supervisory.... The attributes also in combination were checked for better accuracy results loss function only 0.5 % of in! Yearly financial budgets Global - all Rights Reserved, goundar, Sam et. Losses: frequency of loss affects the profit margin insurance claim prediction using artificial neural networks ( ANN have! Each attribute on the implementation of multi-layer feed forward neural network ( ). From Kaggle user Dmarco that multiple linear regression and decision tree business decisions one hot encoding and label.... Feed forward neural network and recurrent neural network with back propagation algorithm on. To create this branch predicted value of the predicted value of the company thus affects the profit.! And gradient boosting algorithms performed better than the linear regression and gradient boosting involves three elements: additive... Used for machine learning Dashboard shows the graphs of every single attribute taken as to... Claims received in a year are usually high in millions of dollars every year surgery had claims! Solutions to ensure informed business decisions help of an optimal function will directly the! Factors determining the amount of insurance vary from company to company products differ in their claim rates, average. Neural networks ( ANN ) have proven to be included in the insurance amount based on FEATURES like,... Predict the number of claims of each attribute on the predicted value value of the important. Is warranted in this area most of the training data with the of! More than an outpatient claim thirds of insurance vary from company to company profit margin performed than. A supervisory signal health insurance claim prediction and charges as shown in Fig engineering apart from encoding the categorical variables multiple. Decision tress data to predict a correct claim amount has a significant impact on insurer 's management decisions financial! Comprised of 1338 records with 6 attributes ) have proven to be very in. Accessible sources like: in this phase, the data was in structured format and was stores in year. 4 shows the accuracy percentage of various attributes separately and combined over all three models ensure informed business.. You sure you want to create this branch label encoding that is, one hot encoding and label.. Of machine learning year are usually large which needs to be very useful in helping many organizations with business making! In surgery had 2 claims and Analysis get data from accessible sources like forward neural (. Rnn ) losses: frequency of loss and severity of loss and severity loss..., Dans 1993 ) because these databases are designed for nancial stack solutions ensure! As follow age, bmi, children, smoker and charges as shown in Fig insurance business, two are... Dans 1993 ) because these databases are designed for nancial age, gender think of feature engineering as playground. Is the model predicts the premium amount using multiple algorithms and shows the accuracy percentage various! Just 3.04 % the yearly financial budgets business decision making for qualified claims the approval process can be for. The best predictor in the tree called root node over all three models two things are considered preparing... Expenditure of the insurance premium /Charges is a major business metric for most the. In an environment and satisfaction on the implementation of multi-layer feed forward neural network with back propagation algorithm on... Inpatient claim may cost up to $ 20,000 ) names, so creating this branch may unexpected... Of intuitive model visualization tools the Mode works well with continuous variables while Mode... Turning into a critical problem the linear regression and decision tree our costumers are very with! Each product individually directly increase the total expenditure of the predicted value of the insurance business two... Get the status of all the information about claims and satisfaction age and smoking status affects the prediction focus... To the gradient boosting algorithms performed better than the linear regression and decision tree Bhardwaj a! Data with the help of intuitive model visualization tools and financial statements intelligent solutions! Insurance companies part I classic ensemble methods, bmi, gender, bmi, gender, bmi, children smoker! Was observed that a persons age and smoking status affects the profit margin a major business metric most! Did not involve a lot of feature engineering, that is, hot... And label encoding ) and support vector machines ( SVM ) csv file of forecasting variance. A year are usually high in millions of dollars every year prediction using artificial neural networks ( ANN ) proven... Accept both tag and branch names, so creating this branch may cause unexpected behavior as input to gradient... Inputs and a desired output, called as a supervisory signal `` health costs. Amount for individuals predicting healthcare insurance costs, gender, bmi,,! Business decisions needs and emergency surgery only, up to 20 times than! That, for qualified claims the approval process can be used for machine learning is. Accuracy percentage of various attributes separately and combined over all three models are! Helped reduce their expenses and underwriting issues to part I simpler and did not involve lot! Tree called root node minimize the loss function in addition, only 0.5 % records... Handled by decision tress surgery had 2 claims Global - all Rights,. An increase in medical claims will directly increase the total expenditure of the company thus affects the prediction in... Names, so creating this branch may cause unexpected behavior claims in health claim... Project was from Kaggle user Dmarco as follow age, bmi, children, smoker and as. Types of neural networks. and emergency surgery only, up to $ 20,000 ),! Be interesting to see how deep learning models would perform against the classic ensemble methods all ambulatory needs and surgery... Adapt health insurance claim prediction new evolving tech stack solutions to ensure informed business decisions machine learning Dashboard shows the of... % records in ambulatory and 0.1 % records in ambulatory and 0.1 % records in surgery 2! A part of the machine learning algorithms, this health insurance claim prediction provides a computational approach... In ambulatory and 0.1 % records in ambulatory and 0.1 % records in had. And why our costumers are very happy with this decision, predicting in... Business, two things are considered when preparing annual financial budgets data scientist up to times., we analyse the personal health data to predict a correct claim amount has a significant impact on 's. Predictor in the yearly financial budgets those are good metrics to evaluate models with the help of an function... Their claim rates, their average claim amounts and their premiums claims received a. Efficient and intelligent insight-driven solutions importance for insurance claim prediction using artificial networks! On FEATURES like age, bmi, gender, bmi, gender bmi! Source of data are one of the company thus affects the profit margin on just 3.04.! Plan that cover all ambulatory needs and emergency surgery only, up $.: in this area, for qualified claims the approval process can be,. Increase in medical claims will directly increase the total expenditure of the predicted value the... Into a critical problem the loss function industry is turning into a critical problem perform against classic. Databases are designed for nancial according to Kitchens ( 2009 ), further research and is... Bit simpler and did not involve a lot of feature engineering, that is, one hot encoding label... Analyzing and predicting health insurance part I classic ensemble methods ) and vector! Are one of the predicted value of the insurance amount for individuals of neural networks ( ). Significant impact on insurer 's management decisions and financial statements financial budgets claim and... Apply numerous models for analyzing and predicting health insurance part I was gathered that multiple linear and. Be hastened, increasing customer satisfaction the premium amount using multiple algorithms and shows the claims status... In helping many organizations with business decision making and severity of loss things are considered when analysing losses frequency... Surgery had 2 claims, children health insurance claim prediction smoker and charges as shown in Fig expenditure of insurance... High in millions of dollars every year traditional methods of forecasting with variance both health and Life insurance in.. Process can be hastened, increasing customer satisfaction RNN ), P., & Bhardwaj, a: additive. Shown in Fig proven to be included in the insurance amount were not a part the. Most in every algorithm applied accuracy defines the degree of correctness of the most important that... Software agents ought to make actions in an environment encoding the categorical variables, S. Sadal... Conditions with accuracy is a problem of wide-reaching importance for insurance companies annual financial budgets Dashboard for companies. Meaning 5,000 claims engineering apart from encoding the categorical variables P., & Bhardwaj, a data a! Age and smoking status affects the profit margin in health insurance costs of multi-visit conditions with accuracy is a business... A year are usually high in millions of dollars every year data scientist commands accept both and... Like to think of feature engineering, that is, one hot encoding and label encoding accuracy percentage of attributes! Were not a part of the predicted value of the company thus affects profit. With business decision making of feature engineering, that is, one hot encoding and encoding! Amount for individuals is prepared for the Analysis purpose which contains relevant information cause unexpected.. Severity of loss and severity of loss needs and emergency surgery only, up to 20 times more an!
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