Logistic regression technique for prediction of cardiovascular disease

Abstract

One of the most life-threatening disease is cardiovascular disease. Its high mortality rate contributes to nearly 17 million deaths all over the world. Early diagnosis helps to treat the disease in timely manner to prevent mortality. There are several machine and deep learning techniques available to classify the presence and absence of the disease. In this research, Logistic Regression (LR) techniques is applied to UCI dataset to classify the cardiac disease. To improve the performance of the model, pre-processing of data by Cleaning the dataset, finding the missing values are done and features selection were performed by correlation with the target value for all the feature. The highly positive correlated features were selected. Then classification is performed by dividing the dataset into training. testing in the ratio of 90:10, 80:20, 70:30, 40:60 and 50:50. The splitting ratio of 90:10 gives best accuracy as listed below. The LR model obtained 87.10% accuracy.