THE ROLE OF SUPERVISED LEARNING IN ENHANCING DIAGNOSTIC ACCURACY OF NEURODEGENERATIVE DISEASES
Keywords:
Supervised Learning, Neurodegenerative Diseases, Alzheimer’s Disease, Parkinson’s Disease, Huntington’s Disease, Machine Learning, SVM, Random Forest, Neural Networks, Medical Imaging, Early Diagnosis, Feature Selection, Diagnostic AccuracyAbstract
Neurodegenerative diseases, including Alzheimer’s, Parkinson’s, and Huntington’s diseases, are characterized by progressive neural degeneration, leading to cognitive and motor impairment. Early diagnosis is critical for effective management and improving patient outcomes, yet traditional diagnostic methods often lack sensitivity in detecting early-stage disease. This study explores the role of supervised learning techniques in enhancing diagnostic accuracy for neurodegenerative diseases. By reviewing key supervised learning models such as support vector machines (SVMs), random forests, and neural networks, this paper evaluates their effectiveness in analyzing complex medical datasets, including MRI, PET scans, and genetic data. The results demonstrate that supervised learning models significantly improve diagnostic accuracy, particularly in early detection. However, challenges such as data availability, overfitting, and ethical concerns must be addressed to ensure widespread clinical application. This paper also presents a comparative analysis of different models, highlighting their performance in terms of accuracy, precision, recall, and F1 scores. The findings underscore the transformative potential of machine learning in neurodegenerative disease diagnostics and suggest directions for future research to overcome existing limitations.
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Copyright (c) 2020 Ashwin Narasimha Murthy, Souptik Sen, Ramesh Krishnamaneni (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
