ArticleMedical & biological engineering & computing2020
Evaluation of train and test performance of machine learning algorithms and Parkinson diagnosis with statistical measurements.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 67 citations in OpenAlex.
- IGLOO: Machine Vision System for Determination of Solubilization Index in Phosphate-Solubilizing Bacteria.Microorganisms · 2025Article
- A New Wrist-Worn Tool Supporting the Diagnosis of Parkinsonian Motor Syndromes.Sensors (Basel, Switzerland) · 2024Article
- Hybrid Machine Learning Framework for Multistage Parkinson's Disease Classification Using Acoustic Features of Sustained Korean Vowels.Bioengineering (Basel, Switzerland) · 2023Article
- Machine Learning-Based Blood RNA Signature for Diagnosis of Autism Spectrum Disorder.International journal of molecular sciences · 2023Article
- A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives.Diagnostics (Basel, Switzerland) · 2022Review
- Review
- Imperative Role of Machine Learning Algorithm for Detection of Parkinson's Disease: Review, Challenges and Recommendations.Diagnostics (Basel, Switzerland) · 2022Review
- Development and validation of a meta-learner for combining statistical and machine learning prediction models in individuals with depression.BMC psychiatry · 2022Article
- Is Speech the New Blood? Recent Progress in AI-Based Disease Detection From Audio in a Nutshell.Frontiers in digital health · 2022Review
- Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease.Frontiers in neurology · 2022Review
- Performance Evaluation of Machine Learning Frameworks for Aphasia Assessment.Sensors (Basel, Switzerland) · 2021Article
- X-Vectors: New Quantitative Biomarkers for Early Parkinson's Disease Detection From Speech.Frontiers in neuroinformatics · 2021Article
- Assessment of Acoustic Features and Machine Learning for Parkinson's Detection.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease is a neurological disorder that causes partial or complete loss of motor reflexes and speech and affects thinking, behavior, and other vital functions affecting the nervous system. Parkinson's disease causes impaired speech and motor abilities (writing, balance, etc.) in about 90% of patients and is often seen in older people. Some signs (deterioration of vocal cords) in medical voice recordings from Parkinson's patients are used to diagnose this disease. The database used in this study contains biomedical speech voice from 31 people of different age and sex related to this disease. The performance comparison of the machine learning algorithms k-Nearest Neighborhood (k-NN), Random Forest, Naive Bayes, and Support Vector Machine classifiers was performed with the used database. Moreover, the best classifier was determined for the diagnosis of Parkinson's disease. Eleven different training and test data (45 × 55, 50 × 50, 55 × 45, 60 × 40, 65 × 35, 70 × 30, 75 × 25, 80 × 20, 85 × 15, 90 × 10, 95 × 5) were processed separately. The data obtained from these training and tests were compared with statistical measurements. The training results of the k-NN classification algorithm were generally 100% successful. The best test result was obtained from Random Forest classifier with 85.81%. All statistical results and measured values are given in detail in the experimental studies section.Graphical abstract.
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