A Machine Learning-Based Clinical Decision Support System for Mental Health Risk Profiling

Abstract

Clinical Decision Support Systems (CDSS) are increasingly being adopted to enhance healthcare delivery, particularly in mental health. This paper presents the design and implementation of a CDSS framework tailored for mental health-related data, focusing on predictive risk profiling and supporting healthcare professionals in data-driven decision-making. The system integrates machine learning algorithms for both classification and regression tasks, facilitating personalized risk assessments and treatment recommendations. It features a modular architecture, consisting of a data processing pipeline, machine learning engine, and an intuitive user interface, allowing for efficient handling of diverse datasets and seamless integration with existing clinical workflows. The system was tested on multiple open datasets, each requiring varying levels of preprocessing and data cleaning. Key results include the performance of models like Random Forest, Gradient Boosting, and K-Nearest Neighbors, and the significant impact of feature complexity over patient volume on processing times. Despite being deployed on mid-range hardware, the system achieved fast response times, highlighting its feasibility for real-time clinical use. The work underscores the importance of usability, performance efficiency, and interoperability in developing CDSS solutions, paving the way for broader adoption in mental health contexts.