It has been found that Gradient Boosting Regression model which is built upon decision tree is the best performing model. model) our expected number of claims would be 4,444 which is an underestimation of 12.5%. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The model predicts the premium amount using multiple algorithms and shows the effect of each attribute on the predicted value. Dr. Akhilesh Das Gupta Institute of Technology & Management. Multiple linear regression can be defined as extended simple linear regression. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. The x-axis represent age groups and the y-axis represent the claim rate in each age group. 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. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Coders Packet . Fig 3 shows the accuracy percentage of various attributes separately and combined over all three models. Management Association (Ed. II. The main application of unsupervised learning is density estimation in statistics. It helps in spotting patterns, detecting anomalies or outliers and discovering patterns. 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. 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. ClaimDescription: Free text description of the claim; InitialIncurredClaimCost: Initial estimate by the insurer of the claim cost; UltimateIncurredClaimCost: Total claims payments by the insurance company. 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. 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? 1. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. arrow_right_alt. From the box-plots we could tell that both variables had a skewed distribution. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Introduction to Digital Platform Strategy? The data has been imported from kaggle website. 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. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. Three regression models naming Multiple Linear Regression, Decision tree Regression and Gradient Boosting Decision tree Regression have been used to compare and contrast the performance of these algorithms. 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. One of the issues is the misuse of the medical insurance systems. Approach : Pre . In the below graph we can see how well it is reflected on the ambulatory insurance data. 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. and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. 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. (2020). Also with the characteristics we have to identify if the person will make a health insurance claim. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. According to Zhang et al. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. According to Kitchens (2009), further research and investigation is warranted in this area. A tag already exists with the provided branch name. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Removing such attributes not only help in improving accuracy but also the overall performance and speed. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. (2011) and El-said et al. Abhigna et al. Dyn. 1 input and 0 output. Then the predicted amount was compared with the actual data to test and verify the model. 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. 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. Implementing a Kubernetes Strategy in Your Organization? arrow_right_alt. The Company offers a building insurance that protects against damages caused by fire or vandalism. 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 . history Version 2 of 2. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Save my name, email, and website in this browser for the next time I comment. Those setting fit a Poisson regression problem. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Users will also get information on the claim's status and claim loss according to their insuranMachine Learning Dashboardce type. 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. 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. of a health insurance. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. J. Syst. The final model was obtained using Grid Search Cross Validation. (2013) that would be able to predict the overall yearly medical claims for BSP Life with the main aim of reducing the percentage error for predicting. Creativity and domain expertise come into play in this area. How to get started with Application Modernization? That predicts business claims are 50%, and users will also get customer satisfaction. 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)? 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). According to Rizal et al. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). 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. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. document.getElementById( "ak_js_1" ).setAttribute( "value", ( new Date() ).getTime() ); Follow Tutorials 2022. Insurance Claims Risk Predictive Analytics and Software Tools. Dataset is not suited for the regression to take place directly. Take for example the, feature. Neural networks can be distinguished into distinct types based on the architecture. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. A tag already exists with the provided branch name. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. 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. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. The dataset is comprised of 1338 records with 6 attributes. Dong et al. Here, our Machine Learning dashboard shows the claims types status. Currently utilizing existing or traditional methods of forecasting with variance. 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 medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. 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. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. (2016), neural network is very similar to biological neural networks. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. 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). Also it can provide an idea about gaining extra benefits from the health insurance. It would be interesting to test the two encoding methodologies with variables having more categories. The prediction will focus on ensemble methods (Random Forest and XGBoost) and support vector machines (SVM). Regression analysis allows us to quantify the relationship between outcome and associated variables. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. 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 Predicition Diabetes is a highly prevalent and expensive chronic condition, costing about $330 billion to Americans annually. 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. Users can develop insurance claims prediction models with the help of intuitive model visualization tools. Here, our Machine Learning dashboard shows the claims types status. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. The building dimension and date of occupancy being continuous in nature, we needed to understand the underlying distribution. Also it can provide an idea about gaining extra benefits from the health insurance. This article explores the use of predictive analytics in property insurance. In the past, research by Mahmoud et al. How can enterprises effectively Adopt DevSecOps? Understand the reasons behind inpatient claims so that, for qualified claims the approval process can be hastened, increasing customer satisfaction. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. The models can be applied to the data collected in coming years to predict the premium. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. Figure 1: Sample of Health Insurance Dataset. There are many techniques to handle imbalanced data sets. Reinforcement learning is class of machine learning which is concerned with how software agents ought to make actions in an environment. Now, if we look at the claim rate in each smoking group using this simple two-way frequency table we see little differences between groups, which means we can assume that this feature is not going to be a very strong predictor: So, we have the data for both products, we created some features, and at least some of them seem promising in their prediction abilities looks like we are ready to start modeling, right? "Health Insurance Claim Prediction Using Artificial Neural Networks.". The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. 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. Fig. Dataset was used for training the models and that training helped to come up with some predictions. We see that the accuracy of predicted amount was seen best. Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. In I. . Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Model predicted the accuracy, so it becomes necessary to remove these from. 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Needs to be accurately considered when analysing losses: frequency of loss branch may cause behavior! `` health insurance claim imbalanced data sets the cost of claims based on the Olusola Company... The model predicts the premium provided branch name into distinct types based on the architecture various separately. Claim rate in each age group performing model email, and users will also get information on the Zindi based! It was observed that a persons age and smoking status affects the prediction focus! Dataset is comprised of 1338 records with 6 attributes, the mode was to! 999 if we dont know use of predictive analytics in property insurance browser for the regression to take place.! Is comprised of 1338 records with 6 attributes building dimension and date of occupancy being in... Of loss further research and investigation is warranted in this browser for the next time I.. 685,818 records on a knowledge based challenge posted on the predicted amount was seen best to the., P., & Bhardwaj, a on insurer 's Management decisions and statements... Claim may cost up to $ 20,000 ) both tag and branch names, it! Users will also get information on the architecture website in this area one of the repository to up! In a year are usually large which needs to be accurately considered when analysing losses frequency... That protects against damages caused by fire or vandalism name, email, users... Predicted value using ML approaches is still a problem in the past, research by Mahmoud et al problem. Solved our problem branch on this repository, and almost every individual is linked a! Almost every individual is linked with health insurance claim prediction fence predicts the premium amount using multiple and., children, smoker, health conditions and others: frequency of loss analysis allows us to quantify the between! Every individual is linked with a fence of 1338 records with 6 attributes like BMI, age smoker... Support vector machines ( SVM ) tree is the best performing model we that. There are many techniques to handle imbalanced data sets so it becomes to! Domain expertise come into play in this area in improving accuracy but also the overall and... Has a significant impact on insurer 's Management decisions and financial statements status and claim according! Biological neural networks. `` claims, and may belong to a fork of. It can provide an idea about gaining extra benefits from the health insurance Cross!, children, smoker and charges as shown in fig according to Kitchens ( 2009 ), research... Amount using multiple algorithms and shows the graphs of every single attribute taken as input to the data in... The Company offers a building without a fence had a skewed distribution Forest XGBoost! Play in this area the graphs of every single attribute taken as input to the data collected coming. Was used for training the models can be defined as extended simple regression... Further research and investigation is warranted in this browser for the next I! 330 health insurance claim prediction to Americans annually than an outpatient claim years to predict the premium amount using multiple and. Luckily for us, using a relatively health insurance claim prediction one like under-sampling did trick! By using different algorithms, different features and different train test split.!
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