health insurance claim prediction
Your email address will not be published. \Codespeedy\Medical-Insurance-Prediction-master\insurance.csv') data.head() Step 2: 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. According to our dataset, age and smoking status has the maximum impact on the amount prediction with smoker being the one attribute with maximum effect. And here, users will get information about the predicted customer satisfaction and claim status. 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. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Either way, looking at the claim rate as a function of the year in which the policy opened, is equivalent to the policys seniority), again looking at the ambulatory product, we clearly see the higher claim rates for older policies, Some of the other features we considered showed possible predictive power, while others seem to have no signal in them. 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. The train set has 7,160 observations while the test data has 3,069 observations. Artificial neural networks (ANN) have proven to be very useful in helping many organizations with business decision making. The dataset is comprised of 1338 records with 6 attributes. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. (2011) and El-said et al. Logs. (2022). According to Kitchens (2009), further research and investigation is warranted in this area. 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. In a dataset not every attribute has an impact on the prediction. This amount needs to be included in the yearly financial budgets. I like to think of feature engineering as the playground of any data scientist. And those are good metrics to evaluate models with. 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 health insurance data was used to develop the three regression models, and the predicted premiums from these models were compared with actual premiums to compare the accuracies of these models. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. Regression or classification models in decision tree regression builds in the form of a tree structure. Comments (7) Run. As you probably understood if you got this far our goal is to predict the number of claims for a specific product in a specific year, based on historic data. 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. J. Syst. By filtering and various machine learning models accuracy can be improved. can Streamline Data Operations and enable 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 key challenge for the insurance industry is to charge each customer an appropriate premium for the risk they represent. i.e. Last modified January 29, 2019, Your email address will not be published. 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. (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. For each of the two products we were given data of years 5 consecutive years and our goal was to predict the number of claims in 6th year. Some of the work investigated the predictive modeling of healthcare cost using several statistical techniques. Early health insurance amount prediction can help in better contemplation of the amount. 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. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Logs. The models can be applied to the data collected in coming years to predict the premium. 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. Most of the cost is attributed to the 'type-2' version of diabetes, which is typically diagnosed in middle age. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. Claim rate is 5%, meaning 5,000 claims. (2016), neural network is very similar to biological neural networks. This sounds like a straight forward regression task!. There are two main methods of encoding adopted during feature engineering, that is, one hot encoding and label encoding. And its also not even the main issue. 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). 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. In addition, only 0.5% of records in ambulatory and 0.1% records in surgery had 2 claims. Neural networks can be distinguished into distinct types based on the architecture. Leverage the True potential of AI-driven implementation to streamline the development of applications. an insurance plan that cover all ambulatory needs and emergency surgery only, up to $20,000). Usually a random part of data is selected from the complete dataset known as training data, or in other words a set of training examples. Accordingly, predicting health insurance costs of multi-visit conditions with accuracy is a problem of wide-reaching importance for insurance companies. With Xenonstack Support, one can build accurate and predictive models on real-time data to better understand the customer for claims and satisfaction and their cost and premium. (2020). This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. 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. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. of a health insurance. Numerical data along with categorical data can be handled by decision tress. Regression analysis allows us to quantify the relationship between outcome and associated variables. However, it is. Specifically the variables with missing values were as follows; Building Dimension (106), Date of Occupancy (508) and GeoCode (102). Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Described below are the benefits of the Machine Learning Dashboard for Insurance Claim Prediction and Analysis. ), Goundar, Sam, et al. 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. This feature equals 1 if the insured smokes, 0 if she doesnt and 999 if we dont know. Abstract In this thesis, we analyse the personal health data to predict insurance amount for individuals. Each plan has its own predefined . Abhigna et al. All Rights Reserved. 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. Currently utilizing existing or traditional methods of forecasting with variance. Health Insurance Claim Prediction Using Artificial Neural Networks: 10.4018/IJSDA.2020070103: A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. 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. Going back to my original point getting good classification metric values is not enough in our case! DATASET USED The primary source of data for this project was . Using this approach, a best model was derived with an accuracy of 0.79. 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. 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. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. According to Zhang et al. We already say how a. model can achieve 97% accuracy on our data. How can enterprises effectively Adopt DevSecOps? 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. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. It also shows the premium status and customer satisfaction every . Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Appl. insurance claim prediction machine learning. Also it can provide an idea about gaining extra benefits from the health insurance. These inconsistencies must be removed before doing any analysis on data. Figure 4: Attributes vs Prediction Graphs Gradient Boosting Regression. (2017) state that artificial neural network (ANN) has been constructed on the human brain structure with very useful and effective pattern classification capabilities. 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. Example, Sangwan et al. Keywords Regression, Premium, Machine Learning. Dataset was used for training the models and that training helped to come up with some predictions. Challenge An inpatient claim may cost up to 20 times more than an outpatient claim. In the next part of this blog well finally get to the modeling process! In health insurance many factors such as pre-existing body condition, family medical history, Body Mass Index (BMI), marital status, location, past insurances etc affects the amount. The data included some ambiguous values which were needed to be removed. This is the field you are asked to predict in the test set. According to Kitchens (2009), further research and investigation is warranted in this area. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Apart from this people can be fooled easily about the amount of the insurance and may unnecessarily buy some expensive health insurance. (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. BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. Then the predicted amount was compared with the actual data to test and verify the model. (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. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. ). 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. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. The ability to predict a correct claim amount has a significant impact on insurer's management decisions and financial statements. Also with the characteristics we have to identify if the person will make a health insurance claim. 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. Health Insurance Claim Prediction Using Artificial Neural Networks. Dong et al. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. (2019) proposed a novel neural network model for health-related . Once training data is in a suitable form to feed to the model, the training and testing phase of the model can proceed. Those setting fit a Poisson regression problem. This research study targets the development and application of an Artificial Neural Network model as proposed by Chapko et al. Health Insurance Cost Predicition. Supervised learning algorithms create a mathematical model according to a set of data that contains both the inputs and the desired outputs. Since the GeoCode was categorical in nature, the mode was chosen to replace the missing values. According to Willis Towers , over two thirds of insurance firms report that predictive analytics have helped reduce their expenses and underwriting issues. Are you sure you want to create this branch? Health Insurance Claim Prediction Using Artificial Neural Networks A. Bhardwaj Published 1 July 2020 Computer Science Int. Yet, it is not clear if an operation was needed or successful, or was it an unnecessary burden for the patient.