An Imperative Diagnostic Model for Predicting CHD using Deep Learning

Coronary heart disease (CHD) is one of the leading cause of an increase in the mortality rate. Various factors are influencing to estimate the presence of CHD in an individual biologically. We made inferences with these influencing factors and their peculiarity by analyzing with a various set of algorithms which help in obtaining a precisive decision support system for the presence of CHD in an individual. We considered a set of linear models (Logistic Regression, Linear Discriminant Analysis), Naïve Bayes, Classification and Regression Trees (CART), Support Vector Machines (SVM). K-Nearest Neighbor (k-NN) for k=1 to 21. Next, we have considered a set of ensemble models. Furtherly, we computed a Multi-Layer Perceptron (MLP) and a Deep Neural Network to evaluate the performance through a deep learning approach. So, with this analysis, we found a linear model (Logistic Regression) tend to give on par results both in the case of Cleveland as well as Framingham dataset. This analysis led to design an intuitive approach for CHD classification and would give insights on how to use them in the medical research field.