European Journal of Molecular & Clinical Medicine

The objective of proposed work is to predict the insurance charges of a person and identify those patients with health insurance policy and medical details weather they have any health issues or not. There are some types of health insurances, which are required to be predicted for a patient. The level of treatment in crisis department vary drastically depending the type of health insurance a person has by this we predict the insurance charges of a person In this paper, a multiple linear regression model for health insurance prediction is proposed. Some factors like age, gender, bmi, smoker, and children were input for developing the linear regression model. The model is very accurate considering that it works with real data from practice, with MAPE (Mean Absolute Percentage Error) around 3%, and coefficient of determination R2=0.7615896. This is significant improvement in comparison to traditional modelsin some recent investigations. The proposed model can be utilized for future health care purpose that process to facilitate the decision making process.

The epidemic of Covid-19 has created a disastrous situation around the globe. The spread of Covid-19 is drastically increasing day by day. Machine learning is one of the efficient tools to track the outbreak of the disease, forecast the probable confirmed and death cases as well as the fatality rate. This study applies multiple regression analysis which is one of the supervised machine learning algorithms to analyze and forecast the fatality rate. The study was conducted to predict the spread of Covid-19 in areas of Telangana, Andhra Pradesh, and India. R-Square (R2), Mean square error (MSE), Root mean square error (RSME) and Mean absolute error (MAE) are the main measures used to predict the accuracy of the algorithm. The results reveal that the case fatality rate is higher in Telangana compared to Andhra Pradesh and India, and more diseased cases are observed in Andhra Pradesh. The study was conducted with the available data; if sufficient data is available then the more precise predictions could be possible using multiple regression analysis.