Robust Voice Feature Selection Using Interval Type-2 Fuzzy AHP for Automated Diagnosis of Parkinson's Disease

Azadi, Hamid; Akbarzadeh-T, Mohammad-R.; Kobravi, Hamid-R; Shoeibi, Ali

doi:10.1109/taslp.2021.3097215

Cited by 15 publications

(12 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Vowel: 59 [ 17 , 40 , 42 , 44 , 48 , 50 , 54 – 59 , 61 – 64 , 68 , 69 , 74 – 85 , 87 – 91 , 93 – 97 , 99 , 100 , 102 , 119 – 122 , 124 , 126 – 128 , 130 – 132 , 135 , 170 , 183 , 184 , 192 ]…”

Section: Resultsunclassified

“…Combined: 45 [ 42 , 44 , 48 , 51 , 52 , 54 , 57 , 62 , 63 , 67 – 70 , 72 , 74 , 76 , 79 , 81 , 85 , 86 , 89 – 91 , 93 , 94 , 96 , 97 , 99 , 100 , 119 , 120 , 122 , 124 , 126 – 131 , 134 , 135 , 170 , 171 , 184 , 192 ]…”

Section: Resultsunclassified

“…CFS f : 31 [ 48 , 51 , 52 , 65 , 67 , 70 , 72 - 74 , 78 - 81 , 83 , 85 , 86 , 88 , 92 , 94 , 96 , 97 , 101 , 102 , 117 , 130 , 131 , 170 , 171 , 180 , 185 ]…”

Section: Resultsunclassified

“…The majority of the studies chose to represent the performance in accuracy, specificity, and sensitivity metrics, which were tied to the overall classification performance in research on voice-affecting disorder diagnosis and monitoring. In this SLR, only 8 results indicated that gender and language diversity may affect the performance in terms of accuracy [ 63 , 89 , 92 , 97 , 114 , 170 - 172 ]. As none of the studies address the effect of unbalanced data on performance evaluation, it is noteworthy that the divergence in accuracy in those outcomes could be the effect of unbalanced data.…”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Idrisoglu¹,

Dallora²,

Anderberg³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

Background Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems. Objective This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest. Methods This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group. Results In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network–based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models. Conclusions This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.

show abstract

Section: Resultsunclassified

“…CFS f : 31 [ 48 , 51 , 52 , 65 , 67 , 70 , 72 - 74 , 78 - 81 , 83 , 85 , 86 , 88 , 92 , 94 , 96 , 97 , 101 , 102 , 117 , 130 , 131 , 170 , 171 , 180 , 185 ]…”

Section: Resultsunclassified

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Idrisoglu¹,

Dallora²,

Anderberg³

et al. 2023

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…The modified Delphi technique has been used to consolidate their evaluations, and the trapezoidal type-2 fuzzy AHP has been emphasized in the calculation of weights of criteria based on the conclusions. A method has been provided by Azadi et al (2021) for discriminating Parkinson’s patients from healthy people by analyzing sound waves that simultaneously consider the influence of five factors utilizing IT2F AHP. A variety of major phonic disturbance metrics, comprising 339 features with varying audio signal properties have been observed as a result.…”

Section: Literature Reviewmentioning

confidence: 99%

Vaccine selection for COVID-19 by AHP and novel VIKOR hybrid approach with interval type-2 fuzzy sets

Özkan

2023

Engineering Applications of Artificial Intelligence

View full text Add to dashboard Cite

SWOT Analysis Integrated Interval Type-2 Triangular Fuzzy AHP: Application on e-Scooter Adaptation in Turkiye

Ayyildiz,

Erdogan

2023

Studies in Fuzziness and Soft Computing

View full text Add to dashboard Cite

Robust Voice Feature Selection Using Interval Type-2 Fuzzy AHP for Automated Diagnosis of Parkinson's Disease

Cited by 15 publications

References 34 publications

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

Vaccine selection for COVID-19 by AHP and novel VIKOR hybrid approach with interval type-2 fuzzy sets

SWOT Analysis Integrated Interval Type-2 Triangular Fuzzy AHP: Application on e-Scooter Adaptation in Turkiye

Contact Info

Product

Resources

About