Captain Sukchayanan. Multi-class classification of metabolic syndrome group using ensemble learning based methods. Master's Degree(Computer Engineering). Mae Fah Luang University. Learning Resources and Educational Media Center. : Mae Fah Luang University, 2023.
Multi-class classification of metabolic syndrome group using ensemble learning based methods
Abstract:
Currently, metabolic syndrome, the leading cause of most non-communicable diseases, has been overlooked because it is common and does not cause illness at the early stage. This study aimed to classify the group of metabolic syndromes including diabetes, cardiovascular disease, and hypertension using an ensemble learning-based method. The data was collected from three sub-districts in the province of Chiang Rai: Mae Khao Tom, Nang Lae, and Tha Sud, with a total of 1,605 records. This study measured model performance using multi-class classification models including Random Forest, Extra Trees, K-Nearest Neighbors, Support Vector Machines, Gradient Boosting, and Decision Tree. Accuracy, Recall, and F1-scores were used to determine performance and the best model values. From the experimental results, Gradient Boosting outperformed other models with 95.25% accuracy, 95.24% precision, 95.26% recall, and 95.24% F1-scores.