Comparative Analysis Using Machine Learning Algorithms to Detect Parkinson’s Disease using Voice Dataset

Parkinson's disease is a degenerative disorder that affects the control of movement, and its symptoms include tremors, stiffness, and difficulties in balance and coordination. This disease results from a decrease in dopamine-producing cells in the brain, and although there is currently no cure for Parkinson's, medication and therapeutic interventions can help alleviate the symptoms. Parkinson's disease usually begins to show its effects in middle to late adulthood. A new measure of dysphonia, Pitch Period Entropy (PPE) assesses the variability or randomness of pitch periods in speech signals, providing insights into the diversity of vocal characteristics. Higher entropy values suggest greater pitch variability in a speaker's voice. PPE is included in the dataset. The methodologies used in this project are Random Forest, Support vector classifier, Extreme gradient boosting (XGB). This comparative study includes performance metrics: Accuracy, precision, Recall, f1 score, and support. The aim is to predict Parkinson's disease and compare performance metrics using various methods.