Identification of Significant Features and Data Mining Techniques in Predicting Heart Stroke

Abstract

The health-care industry generates a large amount of data, which is processed using certain methodologies. One technique that is frequently utilized is data mining. Heart disease is the leading cause of death on a global scale. This system foresees the risk of heart disease developing. The results of this system give you a % likelihood of getting heart disease. Medical parameters are utilized to categories the datasets. This system uses a data mining classification algorithm to analyze such parameters. The datasets are processed in Python programming using two main Machine Learning Algorithms: Decision Tree Approach and Naïve Bayes Algorithm, with the latter showing to be the best algorithm in terms of heart disease accuracy.