Survey on prediction, classification and tracking of neurodegenerative diseases

Abstract

Neurodegenerative diseases (NDD) such as Alzheimer's, Parkinson's, and Huntington's disease are complex conditions that progressively impair neurological function. In recent years, machine learning (ML) techniques have shown considerable promise in the prediction, tracking, and understanding of these diseases, offering potential for earlier diagnosis and better patient outcomes. However, despite the advances, significant challenges remain in accurately predicting and classifying NDD due to their heterogeneous nature and the complexity of underlying biological processes. This survey aims to explore the current developments in the prediction and classification of neurodegenerative diseases using ML. The primary objective is to analyze various methods and techniques employed in the early diagnosis of NDD, focusing on ML algorithms, neuroimaging techniques, and biomarker analysis. The survey systematically reviews and categorizes existing studies, highlighting their methodologies, strengths, and limitations. Through an extensive literature review, the survey identifies key challenges such as the need for large, high-quality datasets, the integration of multi-modal data, and the interpretability of ML models. Findings suggest that while ML holds significant potential for advancing NDD research, addressing these challenges is crucial for its successful application. The survey concludes with a discussion on future research directions, emphasizing the importance of interdisciplinary approaches and the development of robust, transparent, and generalizable ML models for the early detection and diagnosis of neurodegenerative diseases.