A Study on Functional Behavior of Machine Learning Model for Cardiac Disease Classification

Authors

Keywords:

Cardiac Disease, Machine Learning, Feature Selection, Medical domain

Abstract

Cardiac diseases are most prevalent these days with high mortality ratio. The causes and symptoms of the heart diseases also vary according to the type of heart disease. In recent years, Heart disease diagnosis has attracted researchers to provide some automated and online solutions to detect heart disease at an early stage. AI and machine learning algorithms have already contributed a lot in this field and have been proved to be reliable and most efficient. Various feature weight identification and optimization heuristics have also been integrated with machine learning models for detecting heart disorders accurately. This review encapsulates the study on the research works to optimize various stages of machine learning models. It also intends to discuss the significance of pre-processing specially feature selection for machine learning algorithm in detail. The recent advancements in machine learning algorithms, methodologies and the performance gain are also provided in this article.

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Standard Model for Medical Disease Prediction

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Published

17.02.2023

How to Cite

Ritika, Singh, R. ., & Sandeep Dalal. (2023). A Study on Functional Behavior of Machine Learning Model for Cardiac Disease Classification. International Journal of Intelligent Systems and Applications in Engineering, 11(2), 687 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2790

Issue

Vol. 11 No. 2 (2023)

Section

Research Article

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