In supervised learning, a set of input variables, such as blood metabolite or gene expression levels, are used to predict a quantitative response variable like hormone level or a qualitative one ...

and SVM. The RF model got the best performance, whereas the LR model got the worst. Figure 2 Comparison of AUCs among seven machine learning models with bar graph. Figure 3 SHAP summary plot of the 12 ...

Various methods like image processing, machine learning, and deep learning ... algorithm. The SVM algorithm achieved an accuracy of 78% on the same dataset size. Additionally, a histogram graph was ...

Three classification algorithms, namely Random Forest, Gradient Boosting, and Support Vector Machine, were employed ... of variables and the length of the corresponding bar in the bar graph. The ...

In this project, i developed a Cardiac Machine Learning Model ... KNN and SVM algorithms. The results, including accuracy, recall, and specificity, are displayed within the application and visualized ...