article · Scientific Reports
Parkinson's disease is a neurological disorder causing motor and cognitive impairments, with symptoms such as speech and gait difficulties typically appearing around age 50. While no cure exists, medication can manage symptoms, making timely diagnosis crucial. Machine learning and deep learning models can differentiate individuals with Parkinson's disease from healthy controls using voice signal characteristics. Working with a dataset of 195 voice recordings from 31 individuals, techniques such as feature selection, synthetic minority oversampling for class balance, and hyperparameter tuning were applied to optimise detection. A feed-forward neural network achieved 99.11 percent accuracy alongside high precision and recall, while a support vector machine model reached 95.89 percent accuracy. These findings show that computational analysis of voice recordings can reliably detect the condition, highlighting the promise of automated acoustic tools for early diagnosis and treatment planning.
Early identification of Parkinson's disease is vital because timely medication helps manage speech and motor impairments even though no cure exists. Non-invasive detection using voice recordings provides an accessible approach to screening. High-accuracy computational models could allow healthcare providers to recognise indicators of the disease earlier, enabling prompt medical intervention and improving overall quality of life for patients.
The research presents an early-stage algorithmic approach for non-invasive diagnostic support tools based on voice recordings, which could eventually assist clinicians and healthcare services in screening for Parkinson's disease. However, because the models were evaluated on a benchmark dataset of 195 recordings from 31 individuals, the technology remains at an early experimental stage and requires further validation on larger cohorts before clinical deployment or integration into commercial diagnostic software.
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Parkinson's Disease (PD) is a prevalent neurological condition characterized by motor and cognitive impairments, typically manifesting around the age of 50 and presenting symptoms such as gait difficulties and speech impairments. Although a cure remains elusive, symptom management through medication is possible. Timely detection is pivotal for effective disease management. In this study, we leverage Machine Learning (ML) and Deep Learning (DL) techniques, specifically K-Nearest Neighbor (KNN) and Feed-forward Neural Network (FNN) models, to differentiate between individuals with PD and healthy individuals based on voice signal characteristics. Our dataset, sourced from the University of California at Irvine (UCI), comprises 195 voice recordings collected from 31 patients. To optimize model performance, we employ various strategies including Synthetic Minority Over-sampling Technique (SMOTE) for addressing class imbalance, Feature Selection to identify the most relevant features, and hyperparameter tuning using RandomizedSearchCV. Our experimentation reveals that the FNN and KSVM models, trained on an 80-20 split of the dataset for training and testing respectively, yield the most promising results. The FNN model achieves an impressive overall accuracy of 99.11%, with 98.78% recall, 99.96% precision, and a 99.23% f1-score. Similarly, the KSVM model demonstrates strong performance with an overall accuracy of 95.89%, recall of 96.88%, precision of 98.71%, and an f1-score of 97.62%. Overall, our study showcases the efficacy of ML and DL techniques in accurately identifying PD from voice signals, underscoring the potential for these approaches to contribute significantly to early diagnosis and intervention strategies for Parkinson's Disease.
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DOI: 10.1038/s41598-024-64004-9
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