Optimizing Predictive Performance: Hyperparameter Tuning in Stacked Multi-Kernel Support Vector Machine Random Forest Models for Diabetes Identification

Abstract

This study addresses the necessity for more advanced diagnostic tools in managing diabetes, a chronic metabolic disorder that leads to disruptions in glucose, lipid, and protein metabolism caused by insufficient insulin activity. The research investigates the innovative application of machine learning models, specifically Stacked Multi-Kernel Support Vector Machines Random Forest (SMKSVM-RF), to determine their effectiveness in identifying complex patterns in medical data. The innovative ensemble learning method SMKSVM-RF combines the strengths of Support Vector Machines (SVMs) and Random Forests (RFs) to leverage their diversity and complementary features. The SVM component implements multiple kernels to identify unique data patterns, while the RF component consists of an ensemble of decision trees to ensure reliable predictions. Integrating these models into a stacked architecture allows SMKSVM-RF to enhance the overall predictive performance for classification or regression tasks by optimizing their strengths. A significant finding of this study is the introduction of SMKSVM-RF, which displays an impressive 73.37% accuracy rate in the confusion matrix. Additionally, its recall is 71.62%, its precision is 70.13%, and it has a noteworthy F1-Score of 71.34%. This innovative technique shows potential for enhancing current methods and developing into an ideal healthcare system, signifying a noteworthy step forward in diabetes detection. The results emphasize the importance of sophisticated machine learning methods, highlighting how SMKSVM-RF can improve diagnostic precision and aid in the continual advancement of healthcare systems for more effective diabetes management.