Comparing Machine Learning Approaches for Predicting the Severity of Alzheimer Disease

The increasing prevalence of Alzheimer’s disease (AD) necessitates the development of accurate predictive models to determine the severity of this neurodegenerative disorder. This paper provides a comparative analysis of various machine learning (ML) approaches, including traditional algorithms and hybrid models, for predicting the severity of Alzheimer’s disease. The study employs multiple datasets and incorporates feature selection techniques to enhance model performance. We explore and benchmark several ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), and Gradient Boosting Machines (GBM), alongside hybrid models. Our results demonstrate that hybrid models significantly outperform traditional ML approaches in terms of accuracy, sensitivity, and specificity. Moreover, we discuss the implications of these findings for clinical practice and future research directions. This comprehensive analysis underscores the potential of hybrid ML models in the early diagnosis and management of Alzheimer’s disease, ultimately contributing to improved patient outcomes. Future work will focus on integrating longitudinal data and exploring advanced neural network architectures to further refine predictive capabilities.