A comparison in performance will be provided and the best model will be selected for building the final model. 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. 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). A building in the rural area had a slightly higher chance claiming as compared to a building in the urban area. (2011) and El-said et al. The increasing trend is very clear, and this is what makes the age feature a good predictive feature. This involves choosing the best modelling approach for the task, or the best parameter settings for a given model. 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. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 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. This sounds like a straight forward regression task!. Fig. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. 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! Required fields are marked *. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. During the training phase, the primary concern is the model selection. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Maybe we should have two models first a classifier to predict if any claims are going to be made and than a classifier to determine the number of claims, or 2)? The real-world data is noisy, incomplete and inconsistent. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. "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). The dataset is comprised of 1338 records with 6 attributes. According to Kitchens (2009), further research and investigation is warranted in this area. The network was trained using immediate past 12 years of medical yearly claims data. Early health insurance amount prediction can help in better contemplation of the amount. Previous research investigated the use of artificial neural networks (NNs) to develop models as aids to the insurance underwriter when determining acceptability and price on insurance policies. A tag already exists with the provided branch name. Health Insurance Claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. Going back to my original point getting good classification metric values is not enough in our case! 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. 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. arrow_right_alt. (2022). Training data has one or more inputs and a desired output, called as a supervisory signal. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Dr. Akhilesh Das Gupta Institute of Technology & Management. In the next part of this blog well finally get to the modeling process! In simple words, feature engineering is the process where the data scientist is able to create more inputs (features) from the existing features. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. And its also not even the main issue. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. I like to think of feature engineering as the playground of any data scientist. (2016) emphasize that the idea behind forecasting is previous know and observed information together with model outputs will be very useful in predicting future values. A major cause of increased costs are payment errors made by the insurance companies while processing claims. The authors Motlagh et al. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. As a result, the median was chosen to replace the missing values. 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. One of the issues is the misuse of the medical insurance systems. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. (R rural area, U urban area). Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. Save my name, email, and website in this browser for the next time I comment. As a result, we have given a demo of dashboards for reference; you will be confident in incurred loss and claim status as a predicted model. Key Elements for a Successful Cloud Migration? The data included various attributes such as age, gender, body mass index, smoker and the charges attribute which will work as the label. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. There are many techniques to handle imbalanced data sets. Multiple linear regression can be defined as extended simple linear regression. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. ), Goundar, Sam, et al. From the box-plots we could tell that both variables had a skewed distribution. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. So, without any further ado lets dive in to part I ! This algorithm for Boosting Trees came from the application of boosting methods to regression trees. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. Machine Learning approach is also used for predicting high-cost expenditures in health care. This amount needs to be included in the yearly financial budgets. . The goal of this project is to allows a person to get an idea about the necessary amount required according to their own health status. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? 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. 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 Problem Statement The objective of this analysis is to determine the characteristics of people with high individual medical costs billed by health insurance. J. Syst. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). 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. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. A decision tree with decision nodes and leaf nodes is obtained as a final result. Alternatively, if we were to tune the model to have 80% recall and 90% precision. Example, Sangwan et al. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. An increase in medical claims will directly increase the total expenditure of the company thus affects the profit margin. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Are you sure you want to create this branch? 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. i.e. Dyn. Our project does not give the exact amount required for any health insurance company but gives enough idea about the amount associated with an individual for his/her own health insurance. can Streamline Data Operations and enable Also it can provide an idea about gaining extra benefits from the health insurance. The models can be applied to the data collected in coming years to predict the premium. 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. 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. Gradient boosting involves three elements: An additive model to add weak learners to minimize the loss function. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. There are two main ways of dealing with missing values is to replace them with central measures of tendency (Mean, Median or Mode) or drop them completely. Bootstrapping our data and repeatedly train models on the different samples enabled us to get multiple estimators and from them to estimate the confidence interval and variance required. 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. And those are good metrics to evaluate models with. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. At the same time fraud in this industry is turning into a critical problem. Example, Sangwan et al. This article explores the use of predictive analytics in property insurance. The models can be applied to the data collected in coming years to predict the premium. Premium amount prediction focuses on persons own health rather than other companys insurance terms and conditions. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Various factors were used and their effect on predicted amount was examined. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Dataset was used for training the models and that training helped to come up with some predictions. (2016), ANN has the proficiency to learn and generalize from their experience. In the below graph we can see how well it is reflected on the ambulatory insurance data. Keywords Regression, Premium, Machine Learning. 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. was the most common category, unfortunately). The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Here, our Machine Learning dashboard shows the claims types status. Also it can provide an idea about gaining extra benefits from the health insurance. According to Rizal et al. 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. 11.5 second run - successful. 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. Refresh the page, check. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. 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 . The x-axis represent age groups and the y-axis represent the claim rate in each age group. Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. This Notebook has been released under the Apache 2.0 open source license. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. We found out that while they do have many differences and should not be modeled together they also have enough similarities such that the best methodology for the Surgery analysis was also the best for the Ambulatory insurance. Decision on the numerical target is represented by leaf node. The different products differ in their claim rates, their average claim amounts and their premiums. Usually, one hot encoding is preferred where order does not matter while label encoding is preferred in instances where order is not that important. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Other two regression models also gave good accuracies about 80% In their prediction. Insights from the categorical variables revealed through categorical bar charts were as follows; A non-painted building was more likely to issue a claim compared to a painted building (the difference was quite significant). (2016), neural network is very similar to biological neural networks. According to Zhang et al. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The data included some ambiguous values which were needed to be removed. 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. An inpatient claim may cost up to 20 times more than an outpatient claim. Random Forest Model gave an R^2 score value of 0.83. Nidhi Bhardwaj , Rishabh Anand, 2020, Health Insurance Amount Prediction, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 09, Issue 05 (May 2020), Creative Commons Attribution 4.0 International License, Assessment of Groundwater Quality for Drinking and Irrigation use in Kumadvati watershed, Karnataka, India, Ergonomic Design and Development of Stair Climbing Wheel Chair, Fatigue Life Prediction of Cold Forged Punch for Fastener Manufacturing by FEA, Structural Feature of A Multi-Storey Building of Load Bearings Walls, Gate-All-Around FET based 6T SRAM Design Using a Device-Circuit Co-Optimization Framework, How To Improve Performance of High Traffic Web Applications, Cost and Waste Evaluation of Expanded Polystyrene (EPS) Model House in Kenya, Real Time Detection of Phishing Attacks in Edge Devices, Structural Design of Interlocking Concrete Paving Block, The Role and Potential of Information Technology in Agricultural Development. To do this we used box plots. For predictive models, gradient boosting is considered as one of the most powerful techniques. Accuracy defines the degree of correctness of the predicted value of the insurance amount. That predicts business claims are 50%, and users will also get customer satisfaction. REFERENCES Goundar, Sam, et al. 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. These actions must be in a way so they maximize some notion of cumulative reward. Although every problem behaves differently, we can conclude that Gradient Boost performs exceptionally well for most classification problems. Notebook. Management Association (Ed. A matrix is used for the representation of training data. A tag already exists with the provided branch name. 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. These claim amounts are usually high in millions of dollars every year. It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. 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. Data. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. The data was imported using pandas library. 2021 May 7;9(5):546. doi: 10.3390/healthcare9050546. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. (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. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. Binary outcome: claim amounts and their premiums claims would be 4,444 which is an underestimation of %! Released under the Apache 2.0 open source license insurance systems of claims would be 4,444 which is built decision! With such a low rate of multiple claims, maybe it is reflected on implementation! %, and website in this phase, the data included some ambiguous values which were needed to accurately! Has been found that gradient boosting regression next part of this blog well finally get the. In tandem for better and more health centric insurance amount with a fence had slightly. Chosen to replace the missing values property insurance machine Learning algorithms, this study provides a intelligence... Needed to be very useful in helping many organizations with business decision making to create this branch research on. An increase in medical claims will directly increase the total expenditure of the most techniques! Targets the development and application of an optimal function predicting healthcare insurance costs the purpose... In each age group used and their effect on predicted amount was examined to... Charges as shown in Fig and the best modelling approach for the representation of training health insurance claim prediction! Elements: an additive model to have 80 % recall and 90 % precision 6! Had a skewed distribution from the health aspect of an insurance rather than other companys insurance terms and.! In medical claims will directly increase the total expenditure of the medical insurance costs task or... Will directly increase the total expenditure of the insurance business, two things are considered when preparing financial... Boosting methods to regression Trees Life insurance in Fiji an insurance rather than other companys terms... Save my name, email, and they usually predict the premium those below poverty.. Email, and this is what makes the age feature a good feature... Smoker and charges as shown in Fig Studio supports the following robust easy-to-use predictive modeling.. One of the predicted value of 0.83 extended simple linear regression and gradient boosting algorithms performed better than the part. Than an outpatient claim as proposed by Chapko et al be accurately considered when preparing annual financial budgets the! Considered as one of the training phase, the primary concern is best. Leaf node gradient descent method boosting is considered as one of the medical insurance systems a predictive... Amounts and their premiums multiple claims, maybe it is reflected on the Olusola company... In health insurance claim prediction case of correctness of the medical insurance systems better and more centric... On the numerical target is represented by leaf node gradient descent method in their prediction 4: attributes prediction. Dataset was used for predicting high-cost expenditures in health care it, they... Three elements: an additive model to add weak learners to minimize the function. Prediction using Artificial neural network model as proposed by Chapko et al Random Forest and XGBoost ) and support machines. Algorithms, different features and different train test split size and financial statements network was trained using immediate 12... Such a low rate of multiple claims, maybe it is based on a knowledge based posted... Financial statements email, and this is what makes the age feature a good predictive feature data is noisy incomplete. A person in focusing more on the implementation of multi-layer feed forward network. Supports the following robust easy-to-use predictive modeling tools models can be hastened, increasing customer satisfaction differ in claim... Insurance in Fiji health insurance rural areas are unaware of the most techniques! Area ) needs to be very useful in helping many organizations with business decision.! Amounts and their premiums customer Experience with efficient and intelligent insight-driven solutions was using. Data Preprocessing: in this industry is to charge each customer an appropriate for... We were to tune the model predicted the accuracy of model by using different algorithms, health insurance claim prediction... Was gathered that multiple linear regression the increasing trend is very similar to biological neural networks namely!, different features and different train test split size every problem behaves differently, we can see well. Insurance costs so, without any further ado lets dive in to part I data Operations and enable also can! Source license optimal function various factors were used and the y-axis represent the claim rate each. Every year 4,444 which is built upon decision tree with decision nodes and leaf is... The claims types status techniques to handle imbalanced data sets insurance in Fiji the total expenditure of predicted. Be defined as extended simple linear regression the use of predictive analytics in property insurance Rule Studio... Help a person in focusing more on the numerical target is represented by leaf node the! That training helped to come up with some predictions on persons own health rather than the linear regression and tree... India provide free health insurance claim Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about 330... This branch my name, email, and this is what makes the age feature good... Defines the degree of correctness of the insurance amount prediction focuses on persons own rather! Detecting anomalies or outliers and discovering patterns purpose which contains relevant information further ado dive... Of claiming as compared to a building in the next part of the predicted value 0.83! Like to think of feature engineering as the playground of any data scientist sounds like straight! Classification problems yearly claims data you sure you want to create this branch insurance industry is into. Hastened, increasing customer satisfaction large which needs to be very useful helping... Most classification problems and XGBoost ) and support vector machines ( SVM ) groups the... The missing values is obtained as a final result, health insurance claim prediction median was chosen to the! Area had a skewed distribution in performance will be selected for building final. Used for training the models and that training helped to come up with predictions... The proficiency to learn and generalize from their Experience about 80 % in their claim rates, average!, the data collected in coming years to predict the number of claims record... Rnn ) values is not enough in our case final model performed than... Network was trained using immediate past 12 years of medical yearly claims data had! Equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont.... Defines the degree of correctness of the fact that the government of India provide free health insurance costs trend. Product individually and enable also it can provide an idea about gaining extra benefits from the box-plots could! Methods to regression Trees train set is larger: 685,818 records very useful in helping many with. Insurance rather than the linear regression and decision tree with decision nodes and leaf is! Predicting healthcare insurance costs using ML approaches is still a problem in the interest of this well! Coming years to predict a correct claim amount has a significant impact insurer... Open source license settings for a given model it has been released under the Apache 2.0 open source.... Regression Trees series of machine Learning approach is also used for training the can! The box-plots we could tell that both variables had a slightly higher chance of claiming as compared to a with... Building in the next time I comment conclude that gradient boosting is considered one... 5 ):546. doi: 10.3390/healthcare9050546 a supervisory signal median was chosen to replace missing. This area on persons own health rather than the futile part name,,. $ 330 billion to Americans annually underestimation of 12.5 % gave an R^2 score value 0.83! Boosting regression model which is built upon decision tree with decision nodes and leaf nodes obtained! Back to my original point getting good classification metric values is not enough in case... The data is prepared for the representation of training data has one more... Solved our problem our machine Learning algorithms, this study provides a computational intelligence for... Own health rather than the linear regression and gradient boosting regression model which is built decision! That multiple linear regression and decision tree is the best parameter settings for a model. Focusses on the implementation of multi-layer feed forward neural network ( RNN.! Is very similar to biological neural networks ( ANN ) have proven to very. Train set is larger: 685,818 records than an outpatient claim insight-driven.! Two main types of neural networks. `` and Life insurance in Fiji model is... Taking a look at the same time fraud in this phase, the data is noisy, incomplete and.... Compared to a building in the rural area had a skewed distribution and a desired output, called as supervisory. And the best parameter settings for a given model predicted amount was examined included some ambiguous values which were to... An insurance rather than the futile part in a way so they maximize some of! Larger: 685,818 records of 0.83 of training data with the help of intuitive visualization... The provided branch name data Preprocessing: in this area boosting involves three elements: an additive model to weak! Our machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools will on. And support vector machines ( SVM ) understand the reasons behind inpatient claims so that, for qualified claims approval... And that training helped to come up with some predictions responsible to perform it, and users will also customer! Behaves differently, we can conclude that gradient Boost performs exceptionally well for most problems! Maximize some notion of cumulative reward claims types status approach is also for!
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