COMPREHENSIVE ANALYSIS ON COMPARISON OF MACHINE LEARNING AND DEEP LEARNING APPLICATIONS ON CARDIAC ARREST

Abstract

Machine Learning is the technology of having machines to understand and behave as humans do. Refining their learning in supervised manner over time, by feeding them information and data in the form of experiences in the real world. Heart disease has a wide variety of consequences, varying from asymptomatically to extreme arrhythmias, and even premature cardiac failure. A comparative computational analysis was conducted on open-source datasets among the most frequently used classification algorithms in Machine Learning and Neural Networks by randomly splitting data in to test and training and an in-depth survey of feature selection is addressed. Our study further concentrates on working with massive datasets from prospective study.