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). I like to think of feature engineering as the playground of any data scientist. was the most common category, unfortunately). Health insurance is a necessity nowadays, and almost every individual is linked with a government or private health insurance company. Numerical data along with categorical data can be handled by decision tress. (2020). A matrix is used for the representation of training data. An inpatient claim may cost up to 20 times more than an outpatient claim. This research focusses on the implementation of multi-layer feed forward neural network with back propagation algorithm based on gradient descent method. Settlement: Area where the building is located. Accurate prediction gives a chance to reduce financial loss for the company. Factors determining the amount of insurance vary from company to company. (2016), neural network is very similar to biological neural networks. In the next part of this blog well finally get to the modeling process! Also people in rural areas are unaware of the fact that the government of India provide free health insurance to those below poverty line. Based on the inpatient conversion prediction, patient information and early warning systems can be used in the future so that the quality of life and service for patients with diseases such as hypertension, diabetes can be improved. 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. The topmost decision node corresponds to the best predictor in the tree called root node. In I. $$Recall= \frac{True\: positive}{All\: positives} = 0.9 \rightarrow \frac{True\: positive}{5,000} = 0.9 \rightarrow True\: positive = 0.9*5,000=4,500$$, $$Precision = \frac{True\: positive}{True\: positive\: +\: False\: positive} = 0.8 \rightarrow \frac{4,500}{4,500\:+\:False\: positive} = 0.8 \rightarrow False\: positive = 1,125$$, 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! arrow_right_alt. 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. Step 2- Data Preprocessing: In this phase, the data is prepared for the analysis purpose which contains relevant information. Predicting medical insurance costs using ML approaches is still a problem in the healthcare industry that requires investigation and improvement. Refresh the page, check. 1 input and 0 output. One of the issues is the misuse of the medical insurance systems. The algorithm correctly determines the output for inputs that were not a part of the training data with the help of an optimal function. Coders Packet . 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. Logs. The presence of missing, incomplete, or corrupted data leads to wrong results while performing any functions such as count, average, mean etc. 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. Alternatively, if we were to tune the model to have 80% recall and 90% precision. The most prominent predictors in the tree-based models were identified, including diabetes mellitus, age, gout, and medications such as sulfonamides and angiotensins. Many techniques for performing statistical predictions have been developed, but, in this project, three models Multiple Linear Regression (MLR), Decision tree regression and Gradient Boosting Regression were tested and compared. A number of numerical practices exist that actuaries use to predict annual medical claim expense in an insurance company. Fig. The models can be applied to the data collected in coming years to predict the premium. In particular using machine learning, insurers can be able to efficiently screen cases, evaluate them with great accuracy and make accurate cost predictions. To do this we used box plots. Actuaries are the ones who are responsible to perform it, and they usually predict the number of claims of each product individually. Are you sure you want to create this branch? effective Management. Two main types of neural networks are namely feed forward neural network and recurrent neural network (RNN). Notebook. can Streamline Data Operations and enable and more accurate way to find suspicious insurance claims, and it is a promising tool for insurance fraud detection. With such a low rate of multiple claims, maybe it is best to use a classification model with binary outcome: ? BSP Life (Fiji) Ltd. provides both Health and Life Insurance in Fiji. These decision nodes have two or more branches, each representing values for the attribute tested. J. Syst. So, without any further ado lets dive in to part I ! 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. The model predicted the accuracy of model by using different algorithms, different features and different train test split size. Using a series of machine learning algorithms, this study provides a computational intelligence approach for predicting healthcare insurance costs. 99.5% in gradient boosting decision tree regression. Dyn. Results indicate that an artificial NN underwriting model outperformed a linear model and a logistic model. Several factors determine the cost of claims based on health factors like BMI, age, smoker, health conditions and others. Health Insurance Claim Prediction Using Artificial Neural Networks Authors: Akashdeep Bhardwaj University of Petroleum & Energy Studies Abstract and Figures A number of numerical practices exist. Whereas some attributes even decline the accuracy, so it becomes necessary to remove these attributes from the features of the code. This fact underscores the importance of adopting machine learning for any insurance company. (2016), ANN has the proficiency to learn and generalize from their experience. (2011) and El-said et al. In the past, research by Mahmoud et al. 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. 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. Regression or classification models in decision tree regression builds in the form of a tree structure. 2 shows various machine learning types along with their properties. Achieve Unified Customer Experience with efficient and intelligent insight-driven solutions. 1. Taking a look at the distribution of claims per record: This train set is larger: 685,818 records. Regression analysis allows us to quantify the relationship between outcome and associated variables. It is based on a knowledge based challenge posted on the Zindi platform based on the Olusola Insurance Company. Interestingly, there was no difference in performance for both encoding methodologies. Claims received in a year are usually large which needs to be accurately considered when preparing annual financial budgets. 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. Box-plots revealed the presence of outliers in building dimension and date of occupancy. 4 shows the graphs of every single attribute taken as input to the gradient boosting regression model. 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. insurance claim prediction machine learning. (2022). And, to make thing more complicated each insurance company usually offers multiple insurance plans to each product, or to a combination of products. Grid Search is a type of parameter search that exhaustively considers all parameter combinations by leveraging on a cross-validation scheme. 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. The first part includes a quick review the health, Your email address will not be published. Implementing a Kubernetes Strategy in Your Organization? A tag already exists with the provided branch name. In the past, research by Mahmoud et al. Early health insurance amount prediction can help in better contemplation of the amount needed. Attributes are as follow age, gender, bmi, children, smoker and charges as shown in Fig. Sample Insurance Claim Prediction Dataset Data Card Code (16) Discussion (2) About Dataset Content This is "Sample Insurance Claim Prediction Dataset" which based on " [Medical Cost Personal Datasets] [1]" to update sample value on top. the last issue we had to solve, and also the last section of this part of the blog, is that even once we trained the model, got individual predictions, and got the overall claims estimator it wasnt enough. Imbalanced data sets are a known problem in ML and can harm the quality of prediction, especially if one is trying to optimize the, is defined as the fraction of correctly predicted outcomes out of the entire prediction vector. That predicts business claims are 50%, and users will also get customer satisfaction. The network was trained using immediate past 12 years of medical yearly claims data. 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. 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 . And, just as important, to the results and conclusions we got from this POC. 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. And users will also get Customer satisfaction the model to have 80 % recall and %. Use to predict annual medical claim expense in an insurance health insurance claim prediction not a of. Neural networks charges as shown in Fig sure you want to create this branch, to gradient. Numerical practices exist that actuaries use to predict the premium insurance costs to it! 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