Enhancing Depression Predictive Models: a Comparative Study of Hybrid Ai, Machine Learning and Deep Learning Techniques

TL;DR: Enhancing depression predictive models through a comparative study of hybrid AI, machine learning and deep learning techniques. The study revealed a marked superiority of hybrid AI models over standalone ML and DL models in predicting depression with unparalleled accuracy.

Abstract: Abstract In the quest to enhance predictive models for depression, this study introduces a novel comparative analysis of machine learning (ML) and deep learning (DL) techniques, further innovating with the development of hybrid AI models. Leveraging a dataset comprising 2,000 participants, enriched with demographic, socio-economic, behavioral, and clinical variables, including pre- and post-treatment Montgomery-Åsberg Depression Rating Scale (MADRS) scores, we embarked on a comprehensive exploration of factors influencing depression outcomes. Through meticulous data collection, we harmonized diverse variables ranging from basic demographic details to intricate clinical outcomes, paired with a rigorous feature selection process employing Recursive Feature Elimination (RFE) and Principal Component Analysis (PCA). Our analytical journey was underpinned by a robust hyper parameter tuning phase, ensuring the optimization of each model's predictive capacity. The study's core contribution lies in its exhaustive comparison of standalone ML and DL models against our crafted hybrid AI models, revealing a marked superiority of the latter in predicting depression with unparalleled accuracy. The hybrid models, through their synergetic integration of ML and DL methodologies, demonstrated a profound ability to navigate the complexity of depression's multifactorial nature, achieving a perfect prediction accuracy rate in our tests. Our findings advocate for a paradigm shift in predictive analytics for depression, underscoring the potential of hybrid AI models in transcending the limitations of traditional standalone approaches. This research not only paves the way for more nuanced and effective predictive tools in mental health care but also sets a benchmark for future studies at the intersection of biology, psychology, and artificial intelligence. The implications of this work are vast, offering a beacon of hope for personalized and preemptive mental health interventions.