Machine Learning-Based Disease Classification Models for Parkinson’s Based on MRI Images

Abstract

Abstract—Parkinson's disease (PD) is a slowly advancing neurological problem of the central nervous system that is manifested by shaking, rigidity, and slowness of movement. Effective early diagnosis is a must; usually, it includes detailed physical tests and analysis of medical history. This study presents an early-stage Parkinson's disease prediction system based on biological voice characteristics and machine learning (ML). In the study, the researcher will use a publicly accessible dataset that is on Kaggle to discriminate between healthy and affected people using advanced classification methods. Exploratory data analysis (EDA) shows feature correlations and class imbalance, making it possible to advance a systematic data processing pipeline that involves cleaning data, identifying outliers, and standardizing data. This was done in order to improve model performance by removing some features that are not important using feature selection, which reduces dimensionality and computational complexity. They created and assessed two models: Logistic Regression (LR) and Extreme Gradient Boosting (XGBoost), utilizing the ROC curve, F1-score, accuracy, precision, recall, and confusion matrix. The experimental results demonstrated that the XGBoost model outperformed the LR and could be used to make an early diagnosis of Parkinson's disease, with an F1-score of 98.3, an accuracy rate of 97.4, and an AUC of 0.9833. These results demonstrate that XGBoost is a useful diagnostic tool that can assist medical professionals in early Parkinson disease detection.