An Optimized K-Nearest Neighbor Cross Validation Model for Enhanced Prediction of Coronary Heart Disease

Author Reg. ID: TIJER_107465

Published Paper Id: TIJER2307142

Published In: Volume 10 Issue 7, July-2023

Abstract: Coronary heart disease is a major public health issue that affects millions of people worldwide. Despite advances within computational medical treatment and prevention, identifying individuals who are at high risk for developing coronary heart disease remains a challenge due to a low accuracy rate of prediction. Machine learning algorithms, such as k-nearest neighbor, have shown promise in predicting the risk of developing coronary heart disease based on risk factors such as age, sex, smoking status, blood pressure, and cholesterol levels. However, the accuracy of these algorithms can be improved by optimizing the machine learning model classifiers using cross-validation techniques and feature selection methods to solve greater dependence on choosing the initial focal point and optimizing local minimum training. The main objective of this study was to develop an enhanced k-nearest neighbor cross validation model for enhanced prediction of heart disease. The k-Nearest Neighbors algorithm, also known as KNN or k-NN, is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point.

Keywords: Coronary Heart Disease (CHR), k-Nearest neighbor, (KNN), Machine learning (ML), Model Classifiers (MC)

Downloads: 000145

Page No: 265-271

Country: -, -, -

Research Area: Science and Technology

Published Paper URL: https://tijer.org/TIJER/viewpaperforall?paper=TIJER2307142

Published Paper PDF: https://tijer.org/TIJER/papers/TIJER2307142

Click Here to Download This Article

Article Preview