Early Detection of Dementia Disease Using Machine Learning Approach

Detection of dementia is a pressing challenge in modern healthcare, as timely diagnosis significantly improves patient care, treatment outcomes, and resource management. This paper presents a comprehensive approach integrating convolutional neural networks (CNNs) for MRI-based neuroimaging analysis with machine learning algorithms applied to patient data, including cognitive test scores and demographic information. The hybrid model is designed to increase predictive accuracy and reliability. The paper explores the system architecture, problem statement, methodology, testing strategies, and evaluation results. We also discuss future enhancements and challenges in deploying such systems in real-world healthcare environments.