An experimental study for early diagnosing Parkinson's disease using machine learning

TL;DR

This study employs machine learning techniques on clinical, voice, and motor data to accurately diagnose Parkinson's Disease early, achieving high classification accuracy and addressing challenges like data imbalance and overfitting.

Contribution

It introduces a comprehensive ML-based approach for early Parkinson's detection using a public dataset, ensuring data integrity and high accuracy.

Findings

100% accuracy in classifying PD and RBD patients

92% accuracy in classifying PD and healthy controls

Effective data preprocessing to prevent overfitting

Abstract

One of the most catastrophic neurological disorders worldwide is Parkinson's Disease. Along with it, the treatment is complicated and abundantly expensive. The only effective action to control the progression is diagnosing it in the early stage. However, this is challenging because early detection necessitates a large and complex clinical study. This experimental work used Machine Learning techniques to automate the early detection of Parkinson's Disease from clinical characteristics, voice features and motor examination. In this study, we develop ML models utilizing a public dataset of 130 individuals, 30 of whom are untreated Parkinson's Disease patients, 50 of whom are Rapid Eye Movement Sleep Behaviour Disorder patients who are at a greater risk of contracting Parkinson's Disease, and 50 of whom are Healthy Controls. We use MinMax Scaler to rescale the data points, Local Outlier Factor to remove outliers, and SMOTE to balance existing class frequency. Afterwards, apply a number of Machine Learning techniques. We implement the approaches in such a way that data leaking and overfitting are not possible. Finally, obtained 100% accuracy in classifying PD and RBD patients, as well as 92% accuracy in classifying PD and HC individuals.