Abstract: Alzheimer’s disease is a progressive neurological disorder that impairs memory, cognitive functions, and daily activities, making early detection essential for effective treatment and management. This paper presents a predictive analysis model for the early detection of Alzheimer’s disease using clinical and cognitive data. The proposed system employs machine learning techniques to analyze input parameters such as age, body mass index, Mini-Mental State Examination (MMSE) score, memory score, physical activity, sleep quality, and family history. A classification-based approach is used to categorize individuals into four stages: No Alzheimer’s, Cognitively Normal, Mild Cognitive Impairment, and Alzheimer’s Disease. The model is trained and evaluated on a structured dataset, and its performance is assessed using standard metrics including accuracy, precision, recall, and F1-score. In addition, a user-friendly interface is developed to facilitate real-time prediction and improve accessibility. Experimental results demonstrate that the proposed model achieves high accuracy and reliability in early-stage detection. The system can assist healthcare professionals in decision-making and contribute to improved awareness and timely intervention. The study highlights the potential of machine learning techniques in enhancing traditional diagnostic processes and provides a scalable approach for early Alzheimer’s disease prediction.

Downloads: | DOI: 10.17148/IJIREEICE.2026.14443