Heart Failure Prediction System Using Various Approaches: A review

The subfield of Artificial Intelligence (AI) is Machine Learning (ML) which involves developing algorithms for computers to learn from data and make decisions with minimal human intervention. It utilizes data analysis, statistical methods, and other techniques to identify patterns and predict outcom...

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Bibliographic Details
Published in:	2023 1st International Conference on Cognitive Computing and Engineering Education (ICCCEE) pp. 1 - 9
Main Authors:	Jamage, Shridevi K., Mali, Ramesh Y., Shete, Virendra V., Upasani, Dhanjay E.
Format:	Conference Proceeding
Language:	English
Published:	IEEE 27-04-2023
Subjects:	Cardiovascular diseases CVDs ECG Heart heart disease Heart Failure (HF) Machine learning algorithms Medical diagnosis ML algorithms Prediction algorithms Speech recognition Statistical analysis
Online Access:	Get full text
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Summary:	The subfield of Artificial Intelligence (AI) is Machine Learning (ML) which involves developing algorithms for computers to learn from data and make decisions with minimal human intervention. It utilizes data analysis, statistical methods, and other techniques to identify patterns and predict outcomes. ML algorithms are used in various applications, such as medical diagnosis, speech recognition, recommender systems, and natural language processing. Cardiovascular diseases (CVD), including Congestive Heart Failure (CHF) and Coronary Artery Disease (CAD), are the leading cause of death globally. Traditional medical procedures for diagnosing heart disease can be expensive and pose serious health risks. To address this, AI-based systems are being developed to detect and diagnose heart conditions using patient data without invasive procedures, potentially reducing costs and risks associated with traditional diagnostic methods. The paper also examines previous approaches and their limitations, as well as makes recommendations for further research on automated heart disease diagnosis using ML and other forms of data.
DOI:	10.1109/ICCCEE55951.2023.10424646