Prediction of Parkinson's Disease using Machine Learning Techniques on Speech dataset

[...]monitoring progress of the patient and effective diagnosis requires persistent visits by the patient. [...]tests using speech prove to be a good tool to audit PD. This opens up an entirely new door of opportunities for Machine Learning algorithms to work on in-house voice recording datasets of PD patients and then go on to efficiently predict or diagnose PD20. Voice features from the audio clips can be extracted by passing the recordings through signal processing algorithms and a these features can be used to build a classification and regression model to predict a rating on the Unified Parkinson's Disease Rating Scale (UPDRS)15. The paper by Hariharan et.al is different from the rest as in it proposed a hybrid intelligent system3 for detection and subsequent diagnosis of PD. The pre-processing for the proposed system is done using model based clustering (Gaussian Mixture Model), feature reduction is performed using Principal Component Analysis, Linear Discriminant Analysis, Sequential Forward Selection and Sequential Backward Selection.