Optimized Support Vector Machine for Early and Accurate Heart Disease Detection

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Title:	Optimized Support Vector Machine for Early and Accurate Heart Disease Detection
Authors:	Chen, Tzu-chia
Date:	2024-06-13
Issue Date:	2024-07-31 12:14:55 (UTC+8)
Publisher:	CRC Press, London
Abstract:	Many academics use data mining to predict diseases. Some approaches can predict one sickness, while others can predict several. Sickness prediction may be improved. This article provides an overview of the numerous data categorization methods available today. Algorithms represent most commonly. Classifying data involves a lot of computation. To create a disease-fighting plan that works, enormous amounts of data must be analysed. Early diagnosis, severity assessment, and prognosis are frequent. Doing so may postpone disease development, improve quality of life, and lower medical costs. This approach uses machine learning. This article classifies and predicts cardiovascular disease data using machine learning. SVM, ANN, and RF classify heart disease data. Accuracy-wise, SVM is better for heart disease classification and detection.
Relation:	Advancements in Science and Technology for Healthcare, Agriculture, and Environmental Sustainability 1st edition
Appears in Collections:	[Department of Artificial Intelligence] Chapter

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