An Empirical Study of Machine Learning Methods for Analyzing Cardiovascular Disease
Article No.: 8, Pages 1 - 7
Abstract
The entire body depends on the heart to supply plasma to every part of it. Heart disease is diagnosed using conventional medical procedures (like angiography), which come with a greater price tag and serious health hazards. Previously, a range of information collection, information sources, and machine learning techniques were applied. Several study articles dedicated to a specific data standard have been published in multiple previous evaluations. As a result, scientists have created a variety of robotic detection systems using machine learning algorithms in addition to discovery methodologies. Simple, reliable, and efficient methods for detecting cardiovascular disease are provided by ML-based computer-aided diagnostics. In order to assess the prognosis of cardiovascular illness, this effort will analyze computerized diagnostics utilizing a variety of methodologies. With an F-1 score of 98.52, 99.25% precision, and 98.53% accuracy, the Random Forest model outperforms the other models.
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Published In
November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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