Enhancing mental health prognosis: an investigation of advanced hybrid classifiers with cutting-edge feature engineering and fusion strategies

Abstract

Mental health disorders present a significant global challenge, requiring early detection for effective intervention. This research explores the comparative performance of two advanced hybrid classifiers against conventional machine learning models. Introducing an innovative hybrid classifier framework, we combine Support Vector Machines with Neural Networks (Hybrid_1) and Random Forests with Gradient Boosting Machines (Hybrid_2), creating synergistic combinations of traditional and ensemble learning approaches. Using this novel fusion technique, we conduct a comprehensive analysis, emphasizing customized feature engineering strategies tailored for mental health assessment. Evaluation on the Mental_health dataset demonstrates the superior performance of hybrid classifiers, achieving accuracy rates of 86.69% and 93.54% for Hybrid_1 and Hybrid_2, respectively. These results highlight the potential of hybrid classifiers in mental health prediction and emphasize the crucial role of feature engineering in model optimization. Our pioneering hybrids, Hybrid_1 and Hybrid_2, represent a breakthrough, seamlessly integrating Support Vector Machines with Neural Networks and Random Forests with Gradient Boosting Machines, respectively. Distinguished from conventional approaches, our hybrids leverage the combined strengths of diverse algorithms, addressing challenges associated with complex feature relationships and dataset adaptability. This study not only showcases the promise of hybrid classifiers in mental health assessment but also provides valuable insights into feature selection and model interpretability, enhancing our understanding of this critical domain.