Enhancing Cardiovascular Risk Assessment Through Machine Learning

Heart disease is the reason, for mortality. When it comes to addressing concerns, in a facility one of the major challenges is that numerous healthcare professionals lack the necessary expertise and confidence to handle such cases. As a result they tend to make decisions leading to progress and unfo...

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Bibliographic Details
Published in:	2024 IEEE 9th International Conference for Convergence in Technology (I2CT) pp. 1 - 5
Main Authors:	Kandula, Ashok Reddy, Kalyanapu, Srinivas, Vamsi Krishna Alapati, V M S, Mokshagna, Adivanna, Kumar, Ganta Karthik, Satya Sai, Chalamala
Format:	Conference Proceeding
Language:	English
Published:	IEEE 05-04-2024
Subjects:	Heart Heart Disease K-Nearest Neighbour(KNN) Logistic regression Logistic Regression(LR) Machine Learning (ML) Machine learning algorithms Measurement Medical services Naïve Bayes(NB) Random Forest(RF) Support Vector Machine(SVM) Support vector machines Vectors
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Summary:	Heart disease is the reason, for mortality. When it comes to addressing concerns, in a facility one of the major challenges is that numerous healthcare professionals lack the necessary expertise and confidence to handle such cases. As a result they tend to make decisions leading to progress and unfortunately fatal outcomes. In order to address this, cardiac issues have been predicted using ML methods. In this study, many methods like Naïve Baiyes, K-Nearest Neighbour, Logistic Regression, Support Vector Machine, and Random Forest been applied. By the findings of this study, KNN has a maximum accuracy of 84.33% in predicting cardiac complaints based on health factors.
ISBN:	9798350394450
DOI:	10.1109/I2CT61223.2024.10543373