Advancing Neurodegenerative Disorder Diagnosis: A Machine Learning-Driven Evaluation of Assessment Modalities
Keywords:
Neurodegenerative Disorders, Diagnostic Modalities, Dementia, Alzheimer's Disease, Multimodal Assessment, Early Detection, machine learning, SVMAbstract
The global epidemic of neurodegenerative diseases demands a paradigm change in diagnostic approaches. This work initiates a machine learning-focused investigation into the evaluation of neurodegenerative diseases, including dementia and Alzheimer's disease. We present a thorough analysis that combines four different assessment modalities, all enhanced by machine learning algorithms: the classic Clock-drawing Test (CDT), the sophisticated Eye Gaze Analysis, the cognitive Trail-making Test (TMT), and the novel Speech Analysis. This study recognizes the complex range of neurodegenerative illnesses and strategically emphasizes multi-modal evaluation. Given the significant influence these disorders have on patient outcomes, special attention is paid to their early diagnosis. Through a thorough analysis of the advantages and disadvantages of each evaluation method, our research attempts to provide medical practitioners with a machine learning-based framework for accurate diagnosis of neurodegenerative disorders. Our method aims to promote early interventions and enhance patient care in addition to improving diagnostic accuracy. Incorporating machine learning improves diagnostic performance and creates the groundwork for novel advances in the study of neurodegenerative disorders. This comprehensive study advances our knowledge of these illnesses and pave the way for a time when machine learning and sophisticated diagnostics will work together to improve the quality of treatment for people with neurodegenerative diseases.
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