Authors :
Dr. Chandrasekar Vadivelraju; Duttala N Sughanditha Reddy; Korrapati Praneeth Kumar Gowd; Katakaraju Vaahini; Pujari Suresh
Volume/Issue :
Volume 9 - 2024, Issue 1 - January
Google Scholar :
http://tinyurl.com/4c9pmuu7
Scribd :
http://tinyurl.com/mrxna9hm
DOI :
https://doi.org/10.5281/zenodo.10527852
Abstract :
The increasing breakthroughs in illness
diagnosis classification and identification systems have
led to a steady growth in the incorporation of machine
learning in medical diagnostics. These systems provide
crucial data aiding medical professionals in the early
detection of fatal diseases, significantly enhancing
patient survival rates. Globally, heart disease stands as
the leading cause of death. The escalating rates of heart
strokes among juveniles underscore the need for an early
detection system to prevent potential incidents. Frequent
and costly tests like electrocardiograms (ECG) are
impractical for the general population. As a result, a
simple and trustworthy method for estimating the risk of
heart disease is suggested. This system makes use of
machine learning techniques and algorithms including
Support Vector Classifier (SVC), Random Forest, Naïve
Bayes, and K-Nearest Neighbors (KNN). It provides a
useful method of heart disease prediction by analyzing
several factors that users provide through the frontend
interface.
The increasing breakthroughs in illness
diagnosis classification and identification systems have
led to a steady growth in the incorporation of machine
learning in medical diagnostics. These systems provide
crucial data aiding medical professionals in the early
detection of fatal diseases, significantly enhancing
patient survival rates. Globally, heart disease stands as
the leading cause of death. The escalating rates of heart
strokes among juveniles underscore the need for an early
detection system to prevent potential incidents. Frequent
and costly tests like electrocardiograms (ECG) are
impractical for the general population. As a result, a
simple and trustworthy method for estimating the risk of
heart disease is suggested. This system makes use of
machine learning techniques and algorithms including
Support Vector Classifier (SVC), Random Forest, Naïve
Bayes, and K-Nearest Neighbors (KNN). It provides a
useful method of heart disease prediction by analyzing
several factors that users provide through the frontend
interface.
