Machine Learning (ML) is employed for scenario identification. Moreover, accurate ML classification is required to enhance the decision-making process in each communication layer. The proposed model in this study utilizes an enhanced preprocessing phase. The proposed model proves that ...
The classification performance of each machine learning algorithm for each clinical endpoint is summarized in Table 1. Table 1. Summary of the performance of the algorithm considering 100 runs on the balanced datasets for each clinical endpoint in descending order of their mean value. Our results ...
The classification performance of each machine learning algorithm for each clinical endpoint is summarized in Table 1. Table 1. Summary of the performance of the algorithm considering 100 runs on the balanced datasets for each clinical endpoint in descending order of their mean value. Our results ...
The best hyperparameters of GMM obtained are the following: number of components = 4, covariance type = ‘Full’. The supervised learning algorithms RF, KNN, and SVM achieved a classification accuracy of 100%. The number of neighbors in KNN is trivial since the maximum absolute error (MAE) ...