Comparative analysis and prediction of coronary heart disease

Abstract

Cardiovascular disease (CVD) is now one of the leading causes of death worldwide and was also thought to be a serious illness in the mid and old ages. Artificial intelligence and machine learning have a huge impact on the healthcare areas. As a result, getting a familiar individual with data processing techniques suitable for numerical health data. Although, the most often used algorithms for classification tasks will be incredibly advantageous in terms of time management. In particular here, a common procedure has been proposed for predicting cardiovascular disease. Accordingly, we herein consider nine typical classifiers of both machine learning and deep learning technology for the comparative analysis and prediction of coronary heart failure. These models are computationally inexpensive and easy to build. Moreover, these classifiers are tested and compared using a confusion matrix in the Jupyter notebook, yielding classification measures such as accuracy, f1-score, recall, and precision. As a result, the logistic regression classifier gives the maximum possible accuracy, precision, and f1-score of 90.78%, 90.24%, and 91.35% respectively.