Intelligent Dementia Prediction and Patient Monitoring Using Machine Learning and Deep Learning

Dementia is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and behavioral changes, which significantly affect an individual’s independence and quality of life. Early identification of dementia risk factors plays a vital role in slowing disease progression and enabling timely medical intervention. This paper presents an intelligent mobile-based dementia prediction and remote patient monitoring system that integrates deep learning techniques with real-time caregiver support tools. A Recurrent Neural Network (RNN) model is employed to analyze sequential behavioral, cognitive and emotional data for the early detection of cognitive deterioration. The proposed system combines predictive analytics, live location tracking, mood monitoring, and personalized medication reminders within a unified mobile platform to ensure continuous patient supervision. Experimental evaluation demonstrates reliable performance in terms of accuracy, precision, recall and indicating the effectiveness of deep learning models in dementia risk assessment. The system aims to reduce caregiver burden, enhance patient safety and support proactive, data-driven healthcare decision-making through continuous monitoring and early warning mechanisms.