Assessment of Acoustic Features and Machine Learning for Parkinson's Detection

This article presents a machine learning approach for Parkinson's disease detection. Potential multiple acoustic signal features of Parkinson's and control subjects are ascertained. A collaborated feature bank is created through correlated feature selection, Fisher score feature selection, and mutual information-based feature selection schemes. A detection model on top of the feature bank has been developed using the traditional Naive Bayes, which proved state of the art. The Naive Bayes detector on collaborative acoustic features can detect the presence of Parkinson's magnificently with a detection accuracy of 78.97% and precision of 0.926, under the hold-out cross validation. The collaborative feature bank on Naive Bayes revealed distinguishable results as compared to many other recently proposed approaches. The simplicity of Naive Bayes makes the system robust and effective throughout the detection process.