ALZMIND: Machine Learning- Powered Early Diagnosis and Accurate Testing of Alzheimer’s Disease

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder that progressively deteriorates memory, cognition, and behavioral stability. The absence of reliable early diagnostic mechanisms often results in delayed detection and treatment inefficiency. This paper presents ALZMIND, a machine learning-driven digital health platform developed to facilitate early diagnosis and precise cognitive testing for Alzheimer’s disease. The system architecture comprises four integrated components: (i) user registration, authentication, and informed consent management; (ii) role-specific dashboards enabling researchers and patients to monitor and visualize cognitive performance; (iii) a clinical trial matching module that leverages user data and eligibility criteria to recommend relevant ongoing studies; and (iv) a cognitive assessment module employing an interactive memory game and quiz to evaluate recall accuracy, attention span, and recognition ability. The data obtained are analyzed using supervised learning algorithms to detect deviations indicative of early cognitive decline. By combining predictive analytics with user interaction data, ALZMIND aims to enhance diagnostic precision, accelerate research participation, and contribute to early intervention strategies. Experimental results and system evaluation demonstrate the feasibility of integrating artificial intelligence within clinical workflows for neurodegenerative disease management.