Machine Learning for Early Dementia Detection Integrating Clinical Data and MRI Imaging

Dementia, particularly Alzheimer's disease, is a leading cause of cognitive decline, affecting millions globally. Early detection is vital for effective management, yet it remains challenging due to the complex nature of the disease. This paper explores the application of machine learning techniques to predict dementia, focusing on integrating clinical data and MRI images. We propose a hybrid model that combines Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) to enhance prediction accuracy. The study addresses challenges such as data heterogeneity and model interpretability, highlighting the potential of multimodal approaches in dementia research. The results demonstrate the effectiveness of combining clinical and imaging data, offering insights into the future of personalized treatment for dementia.