And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. i.e. age : age of policyholder sex: gender of policy holder (female=0, male=1) Insurance companies apply numerous techniques for analyzing and predicting health insurance costs. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. The network was trained using immediate past 12 years of medical yearly claims data. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. In fact, the term model selection often refers to both of these processes, as, in many cases, various models were tried first and best performing model (with the best performing parameter settings for each model) was selected. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Currently utilizing existing or traditional methods of forecasting with variance. (2011) and El-said et al. Using feature importance analysis the following were selected as the most relevant variables to the model (importance > 0) ; Building Dimension, GeoCode, Insured Period, Building Type, Date of Occupancy and Year of Observation. In the insurance business, two things are considered when analysing losses: frequency of loss and severity of loss. 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. And its also not even the main issue. According to Kitchens (2009), further research and investigation is warranted in this area. The model proposed in this study could be a useful tool for policymakers in predicting the trends of CKD in the 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). These claim amounts are usually high in millions of dollars every year. It also shows the premium status and customer satisfaction every . 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. 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 . A tag already exists with the provided branch name. Whats happening in the mathematical model is each training dataset is represented by an array or vector, known as a feature vector. 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. Appl. The dataset is comprised of 1338 records with 6 attributes. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). These inconsistencies must be removed before doing any analysis on data. Using the final model, the test set was run and a prediction set obtained. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Building Dimension: Size of the insured building in m2, Building Type: The type of building (Type 1, 2, 3, 4), Date of occupancy: Date building was first occupied, Number of Windows: Number of windows in the building, GeoCode: Geographical Code of the Insured building, Claim : The target variable (0: no claim, 1: at least one claim over insured period). The larger the train size, the better is the accuracy. 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. 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. 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. The value of (health insurance) claims data in medical research has often been questioned (Jolins et al. Removing such attributes not only help in improving accuracy but also the overall performance and speed. Refresh the page, check. It was observed that a persons age and smoking status affects the prediction most in every algorithm applied. arrow_right_alt. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. trend was observed for the surgery data). The models can be applied to the data collected in coming years to predict the premium. thats without even mentioning the fact that health claim rates tend to be relatively low and usually range between 1% to 10%,) it is not surprising that predicting the number of health insurance claims in a specific year can be a complicated task. Dataset is not suited for the regression to take place directly. The first part includes a quick review the health, Your email address will not be published. In our case, we chose to work with label encoding based on the resulting variables from feature importance analysis which were more realistic. Are you sure you want to create this branch? Health insurers offer coverage and policies for various products, such as ambulatory, surgery, personal accidents, severe illness, transplants and much more. 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. Where a person can ensure that the amount he/she is going to opt is justified. During the training phase, the primary concern is the model selection. In a dataset not every attribute has an impact on the prediction. The authors Motlagh et al. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! Insurance Companies apply numerous models for analyzing and predicting health insurance cost. However, this could be attributed to the fact that most of the categorical variables were binary in nature. There are many techniques to handle imbalanced data sets. Neural networks can be distinguished into distinct types based on the architecture. If you have some experience in Machine Learning and Data Science you might be asking yourself, so we need to predict for each policy how many claims it will make. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. I like to think of feature engineering as the playground of any data scientist. A building without a garden had a slightly higher chance of claiming as compared to a building with a garden. 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 . Using this approach, a best model was derived with an accuracy of 0.79. Comments (7) Run. 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. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. Fig. Dataset was used for training the models and that training helped to come up with some predictions. 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. Early health insurance amount prediction can help in better contemplation of the amount. 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. Goundar, S., Prakash, S., Sadal, P., & Bhardwaj, A. The dataset is divided or segmented into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. The model was used to predict the insurance amount which would be spent on their health. Dr. Akhilesh Das Gupta Institute of Technology & Management. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. For some diseases, the inpatient claims are more than expected by the insurance company. In this paper, a method was developed, using large-scale health insurance claims data, to predict the number of hospitalization days in a population. Test data that has not been labeled, classified or categorized helps the algorithm to learn from it. Now, lets also say that weve built a mode, and its relatively good: it has 80% precision and 90% recall. A matrix is used for the representation of training data. In I. It was gathered that multiple linear regression and gradient boosting algorithms performed better than the linear regression and decision tree. 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. J. Syst. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. In the interest of this project and to gain more knowledge both encoding methodologies were used and the model evaluated for performance. 1 input and 0 output. The diagnosis set is going to be expanded to include more diseases. 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. Insurance companies are extremely interested in the prediction of the future. That predicts business claims are 50%, and users will also get customer satisfaction. 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). Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. A building without a fence had a slightly higher chance of claiming as compared to a building with a fence. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Accurate prediction gives a chance to reduce financial loss for the company. (2013) and Majhi (2018) on recurrent neural networks (RNNs) have also demonstrated that it is an improved forecasting model for time series. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. ), Goundar, Sam, et al. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. CMSR Data Miner / Machine Learning / Rule Engine Studio supports the following robust easy-to-use predictive modeling tools. It would be interesting to see how deep learning models would perform against the classic ensemble methods. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. 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. 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. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. (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. 11.5s. "Health Insurance Claim Prediction Using Artificial Neural Networks.". Currently utilizing existing or traditional methods of forecasting with variance. Children attribute had almost no effect on the prediction, therefore this attribute was removed from the input to the regression model to support better computation in less time. 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. 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. Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Creativity and domain expertise come into play in this area. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). An inpatient claim may cost up to 20 times more than an outpatient claim. Later the accuracies of these models were compared. A key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. 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. We are building the next-gen data science ecosystem https://www.analyticsvidhya.com. 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. However, training has to be done first with the data associated. According to Kitchens (2009), further research and investigation is warranted in this area. Our data was a bit simpler and did not involve a lot of feature engineering apart from encoding the categorical variables. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. effective Management. Are you sure you want to create this branch? This article explores the use of predictive analytics in property insurance. Claim rate, however, is lower standing on just 3.04%. In the next blog well explain how we were able to achieve this goal. Dong et al. Goundar, Sam, et al. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. This can help a person in focusing more on the health aspect of an insurance rather than the futile part. Abhigna et al. By filtering and various machine learning models accuracy can be improved. The basic idea behind this is to compute a sequence of simple trees, where each successive tree is built for the prediction residuals of the preceding tree. Luckily for us, using a relatively simple one like under-sampling did the trick and solved our problem. The data has been imported from kaggle website. HEALTH_INSURANCE_CLAIM_PREDICTION. This can help not only people but also insurance companies to work in tandem for better and more health centric insurance amount. by admin | Jul 6, 2022 | blog | 0 comments, In this 2-part blog post well try to give you a taste of one of our recently completed POC demonstrating the advantages of using Machine Learning (read here) to predict the future number of claims in two different health insurance product. Now, lets understand why adding precision and recall is not necessarily enough: Say we have 100,000 records on which we have to predict. 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 focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. 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