Aim: The main aim of this research is to detect heart plaque using the Decision Tree algorithm with improved accuracy and comparing it with Least Squares Support Vector Machine. Materials and Methods: Decision tree and Least squares Support Vector Machine algorithms are two groups compared in this study. Each group has 20 samples and calculations utilized pretest power of 0.08 with 95% confidence interval. The G power is estimated for samples using clincalc, which has two groups: alpha, power, and enrollment ratio. These samples are split into two groups: training dataset (n = 489 [70%]) and test dataset (n = 277 [30%]). Results: The accuracy obtained for Decision Tree was 68.13 % 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: It is found that the Decision Tree algorithm is significantly better than the Least Squares Support Vector Machine algorithm in Heart plaque disease detection for the dataset considered.

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.

Aim: The main aim of this research is to detect heart plaque using the Naive Bayes algorithm with improved accuracy and comparing it with Least Squares Support Vector Machine. Materials and Methods: Naive Bayes algorithm and Least squares Support Vector Machine algorithms are two groups compared in this study. In the Kaggle dataset on Heart Plaque Disease, there were a total of 20 samples. Clincalc is used to calculate sample G power of 0.08 with 95% confidence interval. The training dataset (n = 489 (70 %)) and the test dataset (n = 277 (30 %)) are divided into two groups. Result: The accuracy of the Naive Bayes algorithm and the Least Squares Support Vector Machine algorithm is assessed. The Naive Bayes method was 78% accurate, whereas the Least Squares Support Vector Machine method was only 67.3% correct.Conclusion: In this work, the Naive Bayes algorithm outperformed the Least Squares Support Vector Machine algorithm in detecting heart plaque disease in the dataset under consideration. Keywords Heart Plaque disease, Novel intensity feature, Naive Bayes algorithm, Least Squares Support Vector Machine, Prediction, Machine learning.

Aim: The major goal of this study is to compare the effectiveness of the Logistic Regression classifier with the Least Squares Support Vector Machine classifier in detecting plaque in the heart with high accuracy. Materials and Methods: In this work, the Logistic Regression and least squares Support Vector Machine methods are compared. There were a total of 20 samples in the Kaggle dataset on Heart Plaque disease. To calculate sample G power of 0.08 with 95% confidence interval, Clincalc is utilized. There are two groups in the training dataset (n = 489 (70 percent)) and the test dataset (n = 277 (30 percent)). Results: The accuracy of both the Logistic Regression and Least Squares Support Vector Machine algorithms is evaluated. The Least Squares Support Vector Machine approach was only 67.3 % accurate, while the Logistic Regression method was 96 % accurate. Since p(2-tailed) < 0.05, in SPSS statistical analysis, a significant difference exists between the two groups. Conclusion: The Logistic Regression algorithm is significantly better than Least Squares Support Vector Machine algorithm in this study in detecting cardiac plaque disease in the dataset.