Concept of Parkinson Leading to Understanding Mechanisms of the Disease

Neurodegenerative diseases (NDs), such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), are debilitating conditions that affect millions worldwide, and the number of cases is expected to rise significantly in the coming years. Because early detection is crucial for effective intervention strategies, this study investigates whether the structural analysis of selected brain regions, including volumes and their spatial relationships obtained from regular T1-weighted MRI scans (N = 168, PPMI database), can model stages of PD using standard machine learning (ML) techniques. Thus, diverse ML models, including Logistic Regression, Random Forest, Support Vector Classifier, and Rough Sets, were trained and evaluated. Models used volumes, Euclidean, and Cosine distances of subcortical brain structures relative to the thalamus to differentiate among control (HC), prodromal (PR), and PD groups. Based on three separate experiments, the Logistic Regression approach was optimal, providing low feature complexity and strong predictive performance (accuracy: 85%, precision: 88%, recall: 85%) in PD-stage recognition. Using interpretable metrics, such as the volume- and centroid-based spatial distances, models achieved high diagnostic accuracy, presenting a promising framework for early-stage PD identification based on MRI scans.

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Concept of Parkinson Leading to Understanding Mechanisms of the Disease

Cited by 3 publications

References 13 publications

AI Classifications Applied to Neuropsychological Trials in Normal Individuals that Predict Progression to Cognitive Decline

AI Classifications Applied to Neuropsychological Trials in Normal Individuals that Predict Progression to Cognitive Decline

Multi-granular Computing Can Predict Prodromal Alzheimer’s Disease Indications in Normal Subjects

Machine Learning Recognizes Stages of Parkinson’s Disease Using Magnetic Resonance Imaging

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