The objective is to detect patients with Parkinson’s Disease (PD) from the voice samples. The training data includes voice measurements such as average, maximum, and minimum vocal fundamental frequency, several measures of variation in fundamental frequency, variation in amplitudes, ratio of noise to tonal components, signal fractal scaling exponent and nonlinear measures of fundamental frequency variation. From these measurements, feature-selection (filter and wrapper method) is performed to select essential features for detecting Parkinson. After selecting features, different classification algorithms are applied. The best performance (88% accuracy) is achieved using Random Forest with sfs-forward(sequential feature selector). The dataset is collected from: https://archive.ics.uci.edu/ml/datasets/Parkinsons

Classifier:

• Random Forest
• Logistic Regression
• SVC
• Naive Bayes
• KNN

Filter Method:

• chi2
• ANOVA F test
• Mutual Information (MI)

Wrapper Method:

• Recursive Feature Elimination
• Sequential Feature Selector (sfs-forward & sfs-backward)
• Exhaustive Feature Selector

Publication Source: Check the publication for Breast Cancer, Chronic Kidney Disease, and Cardiovascular Disease

This Project’s GitHub Repository

• Classification
• Feature Selection