Mental Health Prediction Using Machine Learning

Abstract

This paper explores how Multimodal Artificial Intelligence (AI) combines diverse medical data—like images, text, physiological signals, and sensor data—to support real-time healthcare decisions. It highlights how integrating multiple data types enhances diagnostic accuracy, speeds up emergency care, improves surgical precision, and assists in chronic and mental health monitoring. The paper discusses fusion techniques (early, late, and intermediate) and key AI models such as CNNs, RNNs, and Transformers used for processing medical data. Major challenges include data integration, computational demands, privacy, and ethical regulation. Looking forward, it emphasizes the importance of explainable AI, personalized medicine, and the use of emerging technologies like 5G, edge computing, and IoMT (Internet of Medical Things). The conclusion asserts that multimodal AI will revolutionize healthcare by enabling precision medicine, proactive care, and better patient outcomes.

Keywords: machine learning, deep learning, edge computing, explainable AI, Clinical Decision Support, Multimodal AI, Healthcare, Real-time Systems, Medical Imaging, Data Fusion, Privacy and Security, Personalized Medicine