Heart Plaque Detection with Improved Accuracy using K-Nearest Neighbors classifier Algorithm in comparison with Least Squares Support Vector Machine

Abstract

Aim: The objective of the work is to evaluate the performance of the k-Nearest Neighbor classifier in detecting heart plaque with high accuracy and comparing it with the Least Squares Support Vector Machine. Materials and Methods: The Kaggle dataset on Heart Plaque Disease yielded a total of 20 samples. Clincalc, which has two groups: alpha, power, and enrollment ratio, is used to assess G power of 0.08 with 95% confidence interval for samples. The training dataset (n = 489 [70 percent]) and the test dataset (n = 277 [30 percent]) are divided into two groups. Accuracy is used to assess the performance of the k-Nearest Neighbor algorithm and the Least Squares Support Vector Machine. Results: The accuracy of the k-Nearest Neighbor algorithm was 86 % and 67.3 % for the Least Squares Support Vector Machine technique. Since p (2-tailed) < 0.05, in SPSS statistical analysis, a significant difference exists between the two groups. Conclusion: In this work, the k-Nearest Neighbor algorithm outperformed the Least Squares Support Vector Machine algorithm in detecting heart plaque disease in the dataset under consideration.