| Contributors: |
Lund University, Faculty of Science, Centre for Mathematical Sciences, Mathematical Statistics, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Matematisk statistik, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: AI and Digitalization, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: AI och digitalisering, Originator, Lund University, Faculty of Medicine, Department of Clinical Sciences, Lund, Section IV, Logopedics, Phoniatrics and Audiology, The voice group, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Lund, Sektion IV, Logopedi, foniatri och audiologi, Röstgruppen, Originator, Lund University, Faculty of Medicine, Department of Clinical Sciences, Lund, Section IV, Logopedics, Phoniatrics and Audiology, Communication and Cognition, Lunds universitet, Medicinska fakulteten, Institutionen för kliniska vetenskaper, Lund, Sektion IV, Logopedi, foniatri och audiologi, Kommunikation och kognition, Originator, Lund University, Faculty of Science, Centre for Mathematical Sciences, Research groups at the Centre for Mathematical Sciences, Statistical Signal Processing Group, Lunds universitet, Naturvetenskapliga fakulteten, Matematikcentrum, Forskargrupper vid Matematikcentrum, -lup-obsolete, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), eSSENCE: The e-Science Collaboration, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), eSSENCE: The e-Science Collaboration, Originator, Lund University, Faculty of Engineering, LTH, LTH Profile areas, LTH Profile Area: Engineering Health, Lunds universitet, Lunds Tekniska Högskola, LTH profilområden, LTH profilområde: Teknik för hälsa, Originator, Lund University, Profile areas and other strong research environments, Strategic research areas (SRA), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Strategiska forskningsområden (SFO), ELLIIT: the Linköping-Lund initiative on IT and mobile communication, Originator, Lund University, Profile areas and other strong research environments, Lund University Profile areas, LU Profile Area: Natural and Artificial Cognition, Lunds universitet, Profilområden och andra starka forskningsmiljöer, Lunds universitets profilområden, LU profilområde: Naturlig och artificiell kognition, Originator |
| Description: |
This study is aimed at detecting Parkinson's disease by analyzing voice measurements made using a mobile phone. The key objectives include creating a model that ensures accurate predictions while maintaining interpretability, consistent with the existing literature on Parkinson's disease. We introduce a novel group-wise scaling method to address typical age and biological sex biases in the datasets, demonstrating 9.5% improvement over conventional scaling for three publicly available data sets. We also show the importance of evaluating the developed model on unseen individuals to achieve reliable classification results. The developed model is shown to offer an accuracy of 82% for unseen individuals, surpassing current state-of-the-art approaches. Furthermore, we offer insights into the decision-making of the model using Shapley additive explanation values. Our analysis reveals that shorter and less variable voiced segments and more variable unvoiced segments, suggesting a monotone voice pattern with frequent pauses, increase the likelihood of classifying the voice as a Parkinson's disease voice. Additionally, greater variability and rate of voiced segments, low variability of unvoiced segments, higher pitch variation, and spectral flux, suggesting continuous phonation and dynamic modulation, correlate with healthy voices. These features align well with the relevant medical literature, confirming our results. The significance of our proposed model lies in its generalizability and reliability for Parkinson's disease detection, potentially decelerating disease progression, reducing healthcare costs, and improving quality of life for patients. |
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