  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.
