Analytical Study on Emerging Trends in Cardiomyopathy Detection through Diverse Database Classification

Abstract

Heart failure (HF) is conditions caused mainly by cardiomyopathy that is a major cause of mortality in the world. New developments in artificial intelligence (AI) here indicate that it can be of big aid when it comes to enhancing the accuracy of cardiomyopathy categorization. This paper provides an extensive analysis of the existing AI approaches towards the classification of cardiomyopathy and categorizes as well as contrasts different classifiers. This means that decision making based on these algorithms can be regarded as accurate or imprecise depending with the chosen datasets used in feeding the algorithm. This research wants to help the scientists in selecting the most suitable classification models for immediate use in the clinical setting. The results shown here clearly prove that machine-learning algorithms coupled with adequate databases can improve diagnostic precision and are feasible for real-time application in the identification of cardiomyopathy subtypes.