Abstract
The correct prediction of heart disease can prevent life threats, and incorrect prediction can prove to be fatal at the same time. In this paper machine learning algorithm is applied to compare the results and analysis of primary dataset. The dataset consists of 46 attributes among these Information gain is used to select 24 features for performing the analysis. Various promising results are achieved and are validated using accuracy and confusion matrix. The dataset consists of some irrelevant features which are handled and data are also normalized for getting better results. Using machine learning approach, 77.78% accuracy was obtained. Multiple linear regressions are used to construct and validate the prediction system. Our experimental result shows that multiple linear regressions are suitable for modelling and predicting cholesterol.