Prediction of Parkinson’s disease based on artificial neural networks using speech datasets

Liu, Wei; Liu, Jierong; Peng, Tao; Wang, Guojun; Bălaş, Valentina Emilia; Geman, Oana; Chiu, Hung-Wen

doi:10.1007/s12652-022-03825-w

Cited by 15 publications

(10 citation statements)

References 32 publications

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“…Similarly, Despotovic et al ( 91 ) achieved a classification accuracy of 96.92% by applying the gaussian processes Automatic Relevance Determination (ARD) to identify individuals with PD using sustained vowels from the Parkinson’s Telemonitoring dataset. Several studies have reported that features extracted from sustained vowels recordings are more effective in discriminating between PD and non-PD cases than features extracted from words or sentences ( 91 , 129 , 130 ). However, several studies have reported that using sentences is more effective than using sustained vowels for classification, with accuracy ranging from 82% to 99%, as sentences provide more insights into prosody, phonation, articulation ( 123 , 131 , 132 ).…”

Section: Methods and Analysismentioning

confidence: 99%

The rise of Parkinson’s disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and “eat, move, sleep” lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

Bhidayasiri,

Sringean,

Phumphid

et al. 2024

Front. Neurol.

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The rising prevalence of Parkinson’s disease (PD) globally presents a significant public health challenge for national healthcare systems, particularly in low-to-middle income countries, such as Thailand, which may have insufficient resources to meet these escalating healthcare needs. There are also many undiagnosed cases of early-stage PD, a period when therapeutic interventions would have the most value and least cost. The traditional “passive” approach, whereby clinicians wait for patients with symptomatic PD to seek treatment, is inadequate. Proactive, early identification of PD will allow timely therapeutic interventions, and digital health technologies can be scaled up in the identification and early diagnosis of cases. The Parkinson’s disease risk survey (TCTR20231025005) aims to evaluate a digital population screening platform to identify undiagnosed PD cases in the Thai population. Recognizing the long prodromal phase of PD, the target demographic for screening is people aged ≥ 40 years, approximately 20 years before the usual emergence of motor symptoms. Thailand has a highly rated healthcare system with an established universal healthcare program for citizens, making it ideal for deploying a national screening program using digital technology. Designed by a multidisciplinary group of PD experts, the digital platform comprises a 20-item questionnaire about PD symptoms along with objective tests of eight digital markers: voice vowel, voice sentences, resting and postural tremor, alternate finger tapping, a “pinch-to-size” test, gait and balance, with performance recorded using a mobile application and smartphone’s sensors. Machine learning tools use the collected data to identify subjects at risk of developing, or with early signs of, PD. This article describes the selection and validation of questionnaire items and digital markers, with results showing the chosen parameters and data analysis methods to be robust, reliable, and reproducible. This digital platform could serve as a model for similar screening strategies for other non-communicable diseases in Thailand.

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Section: Methods and Analysismentioning

confidence: 99%

The rise of Parkinson’s disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and “eat, move, sleep” lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

Bhidayasiri,

Sringean,

Phumphid

et al. 2024

Front. Neurol.

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“…The study employed three datasets: two sourced from the reputable UCI ML Repository (16,17) and one from gshare (18), selected for their demonstrated reliability (11,12,(19)(20)(21)(22)(23)(24). Selection was based on data consistency.…”

Section: Data Acquisitionmentioning

confidence: 99%

Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comparative Study Across Three Datasets

Neto

2024

Preprint

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Objective: This study evaluates the efficacy of voice analysis combined with machine learning (ML) techniques in enabling early, noninvasive diagnosis of Parkinson's Disease (PD). Methods: Voice data, phonation of the vowel 'a', from three distinct datasets (two from the UCI ML Repository and one from figshare) for a total of 432 participants (278 PD patients) were analyzed. We employed four ML models - Artificial Neural Networks (ANN), Random Forest (RF), Gradient Boosting (GB), and Support Vector Machine (SVM) - alongside two ensemble methods (soft voting classifier - EVC and stacking method - ESM). The models underwent 50 iterations of evaluation, involving various data splits and 10-fold cross-validation. Comparative analysis was done using one-way ANOVA followed by Bonferroni post-hoc corrections. Results: The ESM, SVM, and GB models emerged as the top performers, demonstrating superior performance across metrics including accuracy, sensitivity, specificity, precision, F1 score, and ROC AUC. Despite data heterogeneity and variable selection limitations, the models showed high values for all metrics. Conclusion: Machine learning integration with voice analysis, mainly through ESM, SVM, and GB, is promising for early PD diagnosis. Using multi-source data and a large sample size enhances our findings' validity, reliability, and generalizability. Significance: This study underscores the potential of noninvasive voice analysis combined with advanced ML to revolutionize early PD detection and pave the way for cost-effective, scalable diagnostic tools.

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“…To achieve this, they are swiftly shifting towards soft computing tools to evaluate plant performance in a small space [9], [10]. These soft computing tools are more reliable, user-friendly, flexible, and capable of redesigning, reassessing, and decision-making for plant engineers at a low cost and energy [28]- [35].…”

Section: A Backgroundmentioning

confidence: 99%

PID Control Design Using AGPSO Technique and Its Application in TITO Reverse Osmosis Desalination Plant

et al. 2022

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Desalination plants have an important concern regarding controlling the permeate flow rate and pH during operability. This paper proposes the proportional integral derivative (PID) control design using modified Particle Swarm Optimization (PSO) techniques called autonomous groups PSO (AGPSO) in the two-input two-output (TITO) RO desalination plant to control the permeate flow rate and pH. Here, three different versions (AGPSO1, AGPSO2, and AGPSO3) of the AGPSO algorithm are utilized to design PID control for the same TITO plant. In addition, an integral time absolute error (JITAE) based objective function is utilized to design a PID controller. The simulation results suggest that the proposed controller designs are flexible, self-tuning, and have stable characteristics, while the AGPSO3-PID control design attained a robust design for optimum tuning compared to existing improved grey wolf optimization PID (IGWO-PID) and other versions of AGPSO based PIDs (AGPSO1-PID, AGPSO2-PID). The design of AGPSO-PID achieved a minimum objective function than existing IGWO-PID and other versions of AGPSO based PIDs {AGPSO1-PID, AGPSO2-PID}. Finally, the proposed controller designs outperform the existing IGWO-PID design in literature in terms of control performance, demonstrating a precise control for and the improvement of plant performance.

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Prediction of Parkinson’s disease based on artificial neural networks using speech datasets

Cited by 15 publications

References 32 publications

The rise of Parkinson’s disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and “eat, move, sleep” lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

The rise of Parkinson’s disease is a global challenge, but efforts to tackle this must begin at a national level: a protocol for national digital screening and “eat, move, sleep” lifestyle interventions to prevent or slow the rise of non-communicable diseases in Thailand

Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comparative Study Across Three Datasets

PID Control Design Using AGPSO Technique and Its Application in TITO Reverse Osmosis Desalination Plant

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