A Machine Learning Approach for Early Parkinson's Disease Diagnosis Based on Brain Texture Features from T1-Weighted Imaging

This study presents an early diagnostic method for Parkinson’s disease using T1-weighted imaging texture features combined with machine learning models. T1-weighted imaging data from the PPMI database were preprocessed to extract texture features from various brain regions,including the thalamus, hippocampus, caudate nucleus, amygdala, globus pallidus and putamen. The Random Forest (RF) model demonstrated excellent performance in distinguishing Parkinson’s patients from healthy controls, achieving an AUC of 0.90, accuracy of 88.9%, precision of 92.3%, sensitivity of 92.3%, specificity of 80.0%, and an F1 score of 92.3%. A simplified RF model also exhibited strong performance with a prediction accuracy of 77.8%. This method effectively leverages brain texture features to assist in the early diagnosis of Parkinson’s disease.