A Comparison Analysis of Heart Disease Dataset Using Decision Tree and Back-Propagation Network

Heart disease is one of the diseases that are becoming a major cause of mortality throughout the world. A large population in the world is suffering from this problem. Considering the death rate and people suffering from heart diseases, reveals the early diagnosis of heart disease. The health care industry generates terabytes of data every day, which requires proper analysis and prediction of data which can be accomplished through data mining which acts as an intelligent diagnostic tool in heart disease diagnosis. In this research work two data mining classification algorithms are used which are Decision tree and Back-propagation network and are built using Python programming language on Anaconda's Jupyter Notebook. The main purpose of this research is to identify and compare the best classification algorithm with the highest degree of accuracy, which will aid professionals in making decisions and diagnosing the probability of occurrence of heart disease in a patient. Thus preventing the loss of lives at the earliest. The heart disease dataset was obtained from Kaggle with 303 patient records and 14 essential clinical features and the output classifies whether or not a person has heart disease. After the comparative analysis the results proved that Back-propagation gives better results and shows greater accuracy which is 93% as compared to Decision tree.