Detection and Analysis of Cardiovascular Diseases using Machine Learning Techniques

The detection and analysis of cardiovascular diseases is a critical study field within the medical field. Cardiovascular diseases are one of the leading causes of mortality globally, and timely identification is vital to forego adverse effects and improve outcomes for patients with these conditions....

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Bibliographic Details
Published in:	2023 IEEE International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER) pp. 258 - 262
Main Authors:	K, Damodar Prabhu, Rao, Prathiksha, K, Varsha Bhat, N.S, Pooja, Kamath, Priya R
Format:	Conference Proceeding
Language:	English
Published:	IEEE 13-10-2023
Subjects:	Accuracy Artificial intelligence Cardiovascular diseases Machine learning Machine learning algorithms Myocardium Prediction algorithms Predictive models Supervised learning Unsupervised learning Very large scale integration
Online Access:	Get full text
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Summary:	The detection and analysis of cardiovascular diseases is a critical study field within the medical field. Cardiovascular diseases are one of the leading causes of mortality globally, and timely identification is vital to forego adverse effects and improve outcomes for patients with these conditions. The incorporation of machine learning technologies to forecast onset of cardiovascular diseases and analyzing their progression has shown promising results. This paper demonstrates the machine learning models that can provide near-accurate predictions and help identify the right data set based on accuracy. The choice of the best-fitting algorithm has been made by a comparative study and with the inclusion of K-fold cross-validation. We infer that the Random Forest algorithm is the best algorithm for detection and K-Nearest Neighbor Algorithm for the analysis of prominent heart diseases like Stable Angina, ST-elevated myocardial infarction, and non-ST elevated myocardial infarction which will serve as a direction to further diagnosis. Overall, the potential for machine learning to improve early detection and treatment of Cardiovascular Diseases, which could ultimately save lives and reduce healthcare costs, has been recognized. Index Terms-Artificial intelligence, Machine learning, Supervised learning, Unsupervised learning, Accuracy
DOI:	10.1109/DISCOVER58830.2023.10316703