Prediction of Cardiovascular Disease using Multiple Machine Learning Platforms

The number of people affected due to Cardiovascular diseases has escalated in recent years. The sedentary lifestyle, certain genetic factors, obesity, lack of exercise and stressful work environments act as a catalyst in the progress of the disease. Heart failure is one of the Cardio-vascular diseases that occur due to improper flow of blood and inadequate level of oxygen in the blood. Researchers apply machine learning algorithms to identify the crucial factors involved in heart diseases. The data obtained from patients are explored and analyzed using various data mining tools to derive relevant and accurate outcomes. In this paper, two popular machine learning platforms Scikit-Learn and Orange are investigated by implementing Seven machine learning techniques and Boosting algorithms, their performance on the Heart Failure dataset is explored with various training and testing ratios. Their best training and the testing split are determined. Performance of the datamining tools are examined and various metrics are evaluated. Machine learning techniques like traditional Logistic Regression, Naïve Bayes and ensemble Random Forest models had higher prediction accuracies. The Boosting algorithms performed efficiently than other common models with 89%.