A Review on Early Detection and Classification of Heart Disease using Machine Learning Techniques

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Published: Mar 1, 2024
DOI: https://doi.org/10.29070/jmr92f29
Keywords:
Heart Disease, Machine Learning, Decision Tree Algorithm, Naïve Bayes algorithm, Artificial Neural Network (ANN), Logistic Regression , RF and Ensmble learning

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Authors

Shailendra Chaurasia

Research Scholar, Department of Computer Science Engineering, LNCT University, Bhopal, Madhya Pradesh

Dr. Megha Kamble

Professor, Department of Computer Science Engineering, LNCT University, Bhopal, Madhya Pradesh

Abstract

The human heart is the body's second most important organ after the brain, which is given more attention. It supplies and circulates blood throughout the body's organs. Predicting the incidence of cardiac disorders is an important task in the medical domain. Data analytics is helpful for making predictions using more information, and it aids medical centers in making predictions about different diseases. They keep a massive quantity of patient data every month. Predicting the incidence of future diseases may be aided by the recorded data. Predicting an occurrence of cardiovascular disease (CVD) is one use of data mining and ML. Despite being the most prevalent cause of mortality in the contemporary world, early detection of heart disease is notoriously challenging. Several data science challenges may be solved with the use of ML, which incorporates artificial intelligence. One popular use of machine learning is to make predictions using the data that already exists. In addition to summarizing the prior work, this article delves into the current algorithm. This study offers a comprehensive literature review of methods for predicting risk of heart disease, as well as an overview of the healthcare business in relation to heart disease, various diagnostic methodologies, kinds, hazards, and machine learning techniques.

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Issue

Vol. 21 No. 1 (2024): Journal of Advances in Science and Technology (JAST)

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