Visualization analysis and logistic regression based heart disease risk prediction

Abstract

Heart-related illness is the major cause of global death. The optimal solution to tackle this problem and to improve public health is early detection and prevention. Manually diagnosis is tedious and time consuming, which is difficult to be applied for large scale medical inspections, and hence machine learning, computer-based automatic algorithms could be adopted. Logistic regression is a commonly used statistical method for predicting the risk of binary outcomes, such as the presence or absence of heart problems. In this study, logistic regression is leveraged to a dataset of medical records. It is not only developed as an effective model for the early detection of heart disease, but also leveraged for identifying the crucial risk factors of the disease. The results showed that the logistic regression model achieved a high level of accuracy for heart risk prediction, which overall accuracy is 85%. Factors including sex, cholesterol level, age, and blood pressure are observed possessing highest correlations with heart disease.