Modelling an Efficient Heart Disease Prediction System using Norm- and Regularization based Learning Approach

One of the crucial mortality causes in today's world is heart disease. It is essential to predict cardiovascular disease in the region in examining the clinical data. Using the Machine Learning (ML) model is effective to help to make the decisions and expects from the considerable volume of data generated with the help of the health care industry. Different researchers get a sight to predict heart disease using machine learning approaches. A novel method is proposed in this system to find the remarkable attributes by using the machine learning approaches that increase the predicted accuracy of cardiovascular disease. The prediction model has various features and different classification approaches that are known. The level of performance is improved in the proposed system with the level of accuracy of about 99% by the model for prediction having the L1-norm based regularized Support Vector Machine (SVM) for heart disease. Here, MATLAB 2020a is used for simulation, and the model shows superior performance compared to other approaches.