A Systematic Review on Machine Learning-Based Predictive Modeling for Early Risk Detection of Alzheimer’s Disease

Alzheimer's disease (AD) is a progressive neurological condition that, especially in older adults, causes memory loss, cognitive decline, and a lower quality of life. For prompt intervention and better illness treatment, early identification of those who are at risk is essential. The capacity of machine learning (ML) approaches to evaluate complex medical datasets and facilitate early Alzheimer's disease prediction has drawn a lot of attention in recent years. Examining and summarizing previous studies on machine learning-based predictive models created for Alzheimer's disease early risk identification is the goal of this systematic review. The review examines research using machine learning algorithms like Support Vector Machines, Random Forest, Decision Trees, Neural Networks, and Deep Learning techniques that has been published in significant scientific databases. This analysis also covers frequently used datasets, such as clinical records, neuroimaging, cognitive assessment scores, and genetic data. When compared to conventional statistical techniques, the results show that machine learning models can greatly improve the precision and effectiveness of early Alzheimer's risk prediction. However, issues including model interpretability, data heterogeneity, and limited dataset availability continue to be major worries. This work offers a thorough summary of recent developments and suggests future lines of inquiry for creating more dependable and comprehensible machine learning models for early Alzheimer's disease risk assessment.