An experimental study for early diagnosing Parkinson's disease using machine learning
This work addresses the challenge of early detection for Parkinson's disease patients, but it is incremental as it applies existing methods to a specific dataset.
The study tackled early diagnosis of Parkinson's disease by developing machine learning models using clinical, voice, and motor data from a public dataset of 130 individuals, achieving 100% accuracy in classifying Parkinson's disease and Rapid Eye Movement Sleep Behaviour Disorder patients and 92% accuracy in classifying Parkinson's disease and healthy controls.
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.