Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity

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Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings (2013, background): “Such systems lower the inconvenience and cost of physical visits of PD patients to the medical clinic, enable the early diagnosis of the disease, and also lessen the workload of medical personnel [7], [13]–[15].”
Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools (2012, methodology): “The findings of this study agree with those in the machine learning literature strongly endorsing the use of RF in complex applications [21].”
Heart Disease Identification Method Using Machine Learning Classification in E-Healthcare (2020, background): “Moreover, it is expensive and computationally difficult to analyze [8].”
Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson's Disease (2012, methodology): “For that reason, here we investigate many variations of these algorithms which we collectively refer to as jitter and shimmer variants [11].”