  One of the symptoms observed in the early stages of Parkinson's Disease (PD)
is speech impairment. Speech disorders can be used to detect this disease
before it degenerates. This work analyzes speech features and machine learning
approaches to predict PD. Acoustic features such as shimmer and jitter
variants, and Mel Frequency Cepstral Coefficients (MFCC) are extracted from
speech signals. We use two datasets in this work: the MDVR-KCL and the Italian
Parkinson's Voice and Speech database. To separate PD and non-PD speech
signals, seven classification models were implemented: K-Nearest Neighbor,
Decision Trees, Support Vector Machines, Naive Bayes, Logistic Regression,
Gradient Boosting, Random Forests. Three feature sets were used for each of the
models: (a) Acoustic features only, (b) All the acoustic features and MFCC, (c)
Selected subset of features from acoustic features and MFCC. Using all the
acoustic features and MFCC, together with SVM produced the highest performance
with an accuracy of 98% and F1-Score of 99%. When compared with prior art, this
shows a better performance. Our code and related documentation is available in
a public domain repository.
