Predictive Modeling for Dementia's Diagnosis: A Machine Learning Perspective

This study explores predictive modeling for dementia diagnosis using a machine learning framework. Leveraging diverse datasets encompassing clinical, genetic, and neuroimaging information, the research employs feature selection and model optimization techniques. Several machine learning techniques, including Random Forest (RF), Support Vector Machines (SVM), Decision Tree, and other schemes are evaluated for their effectiveness in discerning dementia cases from healthy individuals. Through rigorous validation, the models' performance is assessed and compared, with an exploration of their generalizability across diverse patient cohorts. The study not only aims to enhance diagnostic accuracy but also seeks to unravel complex patterns within heterogeneous datasets, contributing to a deeper understanding of the underlying mechanisms associated with different dementia subtypes. The findings and superiority of Gradient Boost algorithm with highest classification accuracy scores of 84.00% over other techqniques hold promise for revolutionizing dementia diagnosis, providing clinicians with powerful tools for early detection and personalized patient care.