A Review: Machine Learning and Data Mining Approaches for Cardiovascular Disease Diagnosis and Prediction

Abstract

INTRODUCTION: Cardiovascular disease (CVD) is the most common cause of death worldwide, and its prevalence is rising in low-resource settings and among those with lower incomes. OBJECTIVES: Machine learning (ML) algorithms are quickly evolving and being implemented in medical procedures for CVD diagnosis and treatment decisions. Every day, the healthcare business creates massive amounts of data. However, the majority of it is inadequately utilized. Efficient techniques for extracting knowledge from these datasets for clinical diagnosis or other uses are scarce. METHODS: ML is being applied in the healthcare industry all over the world. In the health dataset, ML approaches useful in the prevention of locomotor disorders and heart disease. RESULTS: The revelation of such vital information allows researchers to acquire significant insight into how to use the proper treatment and diagnosis for a specific patient. Researchers study enormous volumes of complex healthcare data using various ML approaches, which improves healthcare professionals in disease prediction. CONCLUSION: The goal of this study is to summarize some of the current research on predicting heart diseases utilizing machine learning and data mining techniques, analyze the various mining algorithm combinations employed, and determine which techniques are useful and efficient. Future directions in prediction systems have also been considered.