A new Diagnosis using a Parkinson's Disease XGBoost and CNN-based classification model Using ML Techniques

Abstract

Parkinson's disease (PD) is a neurological condition that affects the brain of the human body and causes difficultywalking, shaking, stiffness, and loss of balance and coordination. Most of the patients suffering from PD face challenges in speaking during the initial stages. In this study, illness has been classified by applying speech features. The standard speech components employed in Parkinson's Disease are Shimmer, Jitter, Harmonic parameters, Frequency parameters, Detrended Fluctuation Analysis (DFA), Recurrence Period Density Entropy (RPDE), and Pitch Period Entropy (PPE) (PD). These features are the baseline features chosen for this study. CNN and XGBoost have been selected to classify the model andrecognize Parkinson's Disease in the early stages. From the model feature, the selection was excluded to improve the model.