Heart plaque detection with improved accuracy using K-nearest neighbors classifier algorithm in comparison with least squares support vector machine

Cardiometry

article

Vankamaddi Sunil Kumar¹  K Vidhya¹ 

[1] Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical And Technical Sciences, Saveetha University

关键词: Heart Plaque disease;    Novel grayscale texture feature;    K-Nearest Neighbor algorithm;    Least Squares Support Vector Machine;    Prediction;    Machine learning;   

DOI  :  10.18137/cardiometry.2022.25.15901594

学科分类：环境科学（综合）

来源: Russian New University

【 摘 要 】

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.

【 授权许可】

CC BY