{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T16:52:32Z","timestamp":1794070352875,"version":"4.8.0"},"reference-count":87,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,5,1]],"date-time":"2025-05-01T00:00:00Z","timestamp":1746057600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jvoice.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Voice"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1016\/j.jvoice.2022.11.001","type":"journal-article","created":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T15:08:22Z","timestamp":1670684902000},"page":"577-590","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":13,"title":["Different Performances of Machine Learning Models to Classify Dysphonic and Non-Dysphonic Voices"],"prefix":"10.1016","volume":"39","author":[{"given":"Danilo Rangel Arruda","family":"Leite","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8436-8950","authenticated-orcid":false,"given":"Ronei Marcos","family":"de Moraes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9041-7114","authenticated-orcid":false,"given":"Leonardo Wanderley","family":"Lopes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jvoice.2022.11.001_bib0001","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/14015430119969","article-title":"Review: occupational risks for voice problems","volume":"26","author":"Verdolini","year":"2001","journal-title":"Logoped Phoniatr Vocol"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0002","doi-asserted-by":"crossref","first-page":"887","DOI":"10.1044\/2018_AJSLP-17-0009","article-title":"Recommended protocols for instrumental assessment of voice: American speech-language-hearing association expert panel to develop a protocol for instrumental assessment of vocal function","volume":"27","author":"Patel","year":"2018","journal-title":"Am J Speech Lang Pathol"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0003","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1044\/1058-0360(2012\/12-0014)","article-title":"Evidence-based clinical voice assessment: a systematic review","volume":"22","author":"Roy","year":"2013","journal-title":"Am J Speech Lang Pathol"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0004","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.jvoice.2021.03.001","article-title":"Beyond the Buzzwords: artificial Intelligence in Laryngology","volume":"36","author":"Melley","year":"2022","journal-title":"J Voice."},{"key":"10.1016\/j.jvoice.2022.11.001_bib0005","doi-asserted-by":"crossref","unstructured":"Lopes L, Cavalcante D, CoDAS PC. Severity of voice disorders: integration of perceptual and acoustic data in dysphonic patients. SciELO Brasil. 2014. Available at: https:\/\/www.scielo.br\/j\/codas\/a\/kGTm3ryX49stcPVt9YvC5vS\/abstract\/?lang=en. Accessed February 6, 2022.","DOI":"10.1590\/2317-1782\/20142013033"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0006","unstructured":"Lopes L, Sim\u00f5es L, Voice J da SJ. Accuracy of acoustic analysis measurements in the evaluation of patients with different laryngeal diagnoses. Elsevier. 2017. Available at: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0892199716301588. Accessed February 6, 2022."},{"key":"10.1016\/j.jvoice.2022.11.001_bib0007","unstructured":"Lopes L, Vieira V, Costa S, et al. Effectiveness of recurrence quantification measures in discriminating subjects with and without voice disorders. Elsevier. 2020. Available at: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0892199718303448?casa_token=l3factj6UCEAAAAA:9ZyDPtjY6T_FZaAZIAel9LYgTyWZCk2nUFkNEO_wcVwpO1hGFA3QgXQMRt_DGpZevK5nao7Q. Accessed May 12, 2022."},{"key":"10.1016\/j.jvoice.2022.11.001_bib0008","doi-asserted-by":"crossref","first-page":"382.e15","DOI":"10.1016\/j.jvoice.2016.08.015","article-title":"Accuracy of acoustic analysis measurements in the evaluation of patients with different laryngeal diagnoses","volume":"31","author":"Lopes","year":"2017","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0009","unstructured":"Stuart Russell and Peter Norvig - Artificial intelligence: a modern approach. 3rd ed. Available at: https:\/\/www.academia.edu\/download\/61853459\/Artificial-Intelligence-A-Modern-Approach-3rd-Edition-by-Stuart-Russell-Peter-Norvig20200121-107745-13gd7bj.pdf. Accessed July 3, 2022."},{"key":"10.1016\/j.jvoice.2022.11.001_bib0010","unstructured":"Jo T. Machine learning foundations. 2021. Available at: https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-65900-4.pdf. Accessed July 3, 2022."},{"key":"10.1016\/j.jvoice.2022.11.001_bib0011","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1126\/science.aaa8415","article-title":"Machine learning: trends, perspectives, and prospects","volume":"349","author":"Jordan","year":"1979","journal-title":"Science"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0012","unstructured":"Mitchell T, Mitchell T. Machine learning. 1997. Available at: https:\/\/profs.info.uaic.ro\/\u223cciortuz\/SLIDES\/2017s\/ml0.pdf. Accessed July 3, 2022."},{"issue":"6","key":"10.1016\/j.jvoice.2022.11.001_bib0013","doi-asserted-by":"crossref","first-page":"947.e11","DOI":"10.1016\/j.jvoice.2018.07.014","article-title":"A survey on machine learning approaches for automatic detection of voice disorders","volume":"33","author":"Hegde","year":"2019","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0014","doi-asserted-by":"crossref","first-page":"6961","DOI":"10.1109\/ACCESS.2017.2696056","article-title":"Voice pathology detection and classification using auto- correlation and entropy features in different frequency regions","volume":"6","author":"Al-Nasheri","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0015","first-page":"31","article-title":"An investigation of multidimensional voice program parameters in three different databases for voice pathology detection and classification","author":"Al-nasheri","year":"2017","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0016","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1001\/jamaoto.2018.0980","article-title":"Sociodemographic characteristics and treatment response among aging adults with voice disorders in the United States","volume":"144","author":"Bertelsen","year":"2018","journal-title":"JAMA Otolaryngol Head Neck Surg"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0017","doi-asserted-by":"crossref","first-page":"2093","DOI":"10.1002\/lary.26465","article-title":"Voice disorders and associated risk markers among young adults in the United States","volume":"127","author":"Bainbridge","year":"2017","journal-title":"Laryngoscope."},{"key":"10.1016\/j.jvoice.2022.11.001_bib0018","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1177\/0003489420956379","article-title":"Patient-reported factors associated with the onset of hyperfunctional voice disorders","volume":"130","author":"Kridgen","year":"2021","journal-title":"Ann Otology Rhinol Laryngol"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0019","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1109\/10.486257","article-title":"Direct speech feature estimation using an iterative EM algorithm for vocal fold pathology detection","volume":"43","author":"Gavidia-Ceballos","year":"1996","journal-title":"IEEE Trans Biomed Eng"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0020","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/S1350-4533(02)00064-4","article-title":"Pathological voice quality assessment using artificial neural networks","volume":"24","author":"Ritchings","year":"2002","journal-title":"Med Eng Phys"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0021","series-title":"Identification of voice disorders using speech samples","first-page":"951","author":"Nayak","year":"2003"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0022","series-title":"K-means Nearest Neighbor Classifier for Voice Pathology","first-page":"352","author":"Ananthakrishna","year":"2004"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0023","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1109\/TBME.2003.820386","article-title":"Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors","volume":"51","author":"Godino-Llorente","year":"2004","journal-title":"IEEE Trans Biomed Eng"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0024","series-title":"Comparison of Neural Networks and Support Vector Machines Applied to Optimized Features Extracted From Patients\u2019 Speech Signal for Classification of Vocal Fold Inflammation","first-page":"844","author":"Behroozmand","year":"2005"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0025","series-title":"Discrete Wavelet Transform and Support Vector Machine Applied to Pathological Voice Signals Identification","first-page":"5","author":"Fonseca","year":"2005"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0026","first-page":"1260","article-title":"Automatic diagnosis of pathological voices","volume":"2","author":"Schlotthauer","year":"2006","journal-title":"WSEAS Trans Signal Process"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0027","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.compbiomed.2006.08.016","article-title":"Optimal selection of wavelet-packet-based features using genetic algorithm in pathological assessment of patients\u2019 speech signal with unilateral vocal fold paralysis","volume":"37","author":"Behroozmand","year":"2007","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0028","doi-asserted-by":"crossref","first-page":"468","DOI":"10.1109\/TBME.2005.869776","article-title":"Telephony-based voice pathology assessment using automated speech analysis","volume":"53","author":"Moran","year":"2006","journal-title":"IEEE Trans Biomed Eng"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0029","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.compbiomed.2015.07.026","article-title":"Voice data mining for laryngeal pathology assessment","volume":"69","author":"Hemmerling","year":"2016","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0030","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.1016\/j.eswa.2009.06.040","article-title":"A comparison of multiple classification methods for diagnosis of Parkinson disease","volume":"37","author":"Das","year":"2010","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0031","unstructured":"Wroge T, \u00d6zkanca Y, Demiroglu C, et al. Parkinson's disease diagnosis using machine learning and voice. 2018. Available at: https:\/\/ieeexplore.ieee.org\/abstract\/document\/8615607. Accessed February 8, 2022."},{"key":"10.1016\/j.jvoice.2022.11.001_bib0032","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.neucom.2015.07.138","article-title":"An efficient hybrid kernel extreme learning machine approach for early diagnosis of Parkinson\u05f3 s disease","volume":"184","author":"Chen","year":"2016","journal-title":"Neurocomputing"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0033","doi-asserted-by":"crossref","first-page":"1820","DOI":"10.1109\/JBHI.2015.2467375","article-title":"Characterization methods for the detection of multiple voice disorders: neurological, functional, and laryngeal diseases","volume":"19","author":"Orozco-Arroyave","year":"2015","journal-title":"IEEE J Biomed Health Inf"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0034","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1007\/s11517-013-1051-8","article-title":"Pathological speech signal analysis and classification using empirical mode decomposition","volume":"51","author":"Kaleem","year":"2013","journal-title":"Med Biol Eng Comput"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0035","article-title":"Objective assessment of pathological voice using artificial intelligence based on the GRBAS scale","author":"Kojima","year":"2021","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0036","unstructured":"Fezari M, Amara F. Acoustic analysis for detection of voice disorders using adaptive features and classifiers. 2014. Available at: https:\/\/www.researchgate.net\/profile\/Mohamed-Fezari-2\/publication\/272093756_wwwinaseorg_library_2014_interlaken_bypaper_CSC_CSC-19\/links\/54db0ae00cf2ba88a68ee10a\/wwwinaseorg-library-2014-interlaken-bypaper-CSC-CSC-19.pdf. Accessed May 18, 2022."},{"issue":"11","key":"10.1016\/j.jvoice.2022.11.001_bib0037","doi-asserted-by":"crossref","first-page":"1280","DOI":"10.1134\/S1064226914110059","article-title":"Towards developing a voice pathologies detection system","volume":"59","author":"el Emary","year":"2014","journal-title":"J Commun Technol Electron"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0038","unstructured":"Chen L, Wang C, Chen J, et al. Voice disorder identification by using hilbert-huang transform (HHT) and K Nearest Neighbor (KNN). Elsevier. 2021. Available at: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0892199720301016?casa_token=4K5XDK2tDzEAAAAA:KpXPOyAXyhRkL5XxgqNICGmjhmJIU2nSxy39zv7bd2Qn_zOI04Ho1xyuJEgXRmqYKEY6k7DJ. Accessed May 18, 2022."},{"key":"10.1016\/j.jvoice.2022.11.001_bib0039","first-page":"6","article-title":"Disease detection using analysis of voice parameters","volume":"4","author":"Sonu","year":"2012","journal-title":"Int J Comput Sci Commun Technol"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0040","doi-asserted-by":"crossref","unstructured":"Kadiri S, Alku P. Mel-frequency cepstral coefficients of voice source waveforms for classification of phonation types in speech. 2019. p. 2508\u20132512. https:\/\/www.apiit.edu.in\/downloads\/all%20chapters\/CHAPTER-91.pdf","DOI":"10.21437\/Interspeech.2019-2863"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0041","doi-asserted-by":"crossref","first-page":"60","DOI":"10.3109\/14015439.2010.528788","article-title":"On combining information from modulation spectra and mel-frequency cepstral coefficients for automatic detection of pathological voices","volume":"36","author":"Arias-Londo\u00f1o","year":"2011","journal-title":"Logopedics Phoniatrics Vocol"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0042","unstructured":"Kantardzic M. Data Reduction. New York, NY:John Wiley & Sons, Inc; 2003:53\u201386."},{"key":"10.1016\/j.jvoice.2022.11.001_bib0043","doi-asserted-by":"crossref","unstructured":"Chen L, Wang C, Chen J, et al. Voice disorder identification by using hilbert-huang transform (HHT) and K nearest neighbor (KNN). J Voice. 2020;35(6)","DOI":"10.1016\/j.jvoice.2020.03.009"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0044","series-title":"Feature Analysis for Automatic Detection of Pathological Speech","first-page":"182","author":"Dibazar","year":"2002"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0045","series-title":"Feature Extraction Based on Mel-Scaled Wavelet Packet Transform for the Diagnosis of Voice Disorders","first-page":"790","author":"Murugesapandian","year":"2008"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0046","doi-asserted-by":"crossref","DOI":"10.1155\/2009\/928974","article-title":"A joint time-frequency and matrix decomposition feature extraction methodology for pathological voice classification","volume":"2009","author":"Ghoraani","year":"2009","journal-title":"EURASIP J Adv Signal Process"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0047","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1504\/IJBET.2011.040452","article-title":"Detection of vocal fold paralysis and edema using time-domain features and probabilistic neural network","volume":"6","author":"Hariharan","year":"2011","journal-title":"Int J Biomed Eng Technol"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0048","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/TASL.2010.2104141","article-title":"Voice pathology detection and discrimination based on modulation spectral features","volume":"19","author":"Markaki","year":"2011","journal-title":"IEEE Trans Audio Speech Lang Process"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0049","unstructured":"Tsanas A, Little MA, McSharry PE, et al. Novel speech signal processing algorithms for high-accuracy classification of Parkinson's disease. 2012. Available at: https:\/\/ieeexplore.ieee.org\/abstract\/document\/6126094\/. Accessed May 19, 2022."},{"key":"10.1016\/j.jvoice.2022.11.001_bib0050","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.bspc.2011.03.010","article-title":"An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine","volume":"7","author":"Arjmandi","year":"2012","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0051","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.bspc.2013.11.002","article-title":"An efficient voice pathology classification scheme based on applying multi-layer linear discriminant analysis to wavelet packet-based features","volume":"10","author":"Akbari","year":"2014","journal-title":"Biomed Signal Process Control"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0052","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.jvoice.2018.02.003","article-title":"Detection of pathological voice using cepstrum vectors: a deep learning approach","volume":"33","author":"Fang","year":"2019","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0053","article-title":"Differences and reliability of linear and nonlinear acoustic measures as a function of vocal intensity in individuals with voice disorders","author":"Florencio","year":"2021","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0054","doi-asserted-by":"crossref","first-page":"806.e1","DOI":"10.1016\/j.jvoice.2019.01.004","article-title":"What makes the cepstral peak prominence different to other acoustic correlates of vocal quality?","volume":"34","author":"Ferrer Riesgo","year":"2020","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0055","article-title":"Voice feature selection to improve performance of machine learning models for voice production inversion","author":"Zhang","year":"2021","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0056","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.jvoice.2015.06.010","article-title":"Analysis and classification of voice pathologies using glottal signal parameters","volume":"30","author":"Forero","year":"2016","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0057","doi-asserted-by":"crossref","first-page":"485","DOI":"10.1016\/j.jvoice.2004.07.006","article-title":"Endoscope motion compensation for laryngeal high-speed videoendoscopy","volume":"19","author":"Deliyski","year":"2005","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0058","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1590\/2317-1782\/20152014144","article-title":"Characteristics of voice and personality of patients with vocal fold immobility","volume":"27","author":"de Almeida","year":"2015","journal-title":"Codas"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0059","first-page":"951","article-title":"Recommended protocols for instrumental assessment of voice: american speech- language-hearing association expert panel to develop a protocol for instrumental assessment of vocal function","volume":"6","author":"Heckman","year":"1967","journal-title":"Angew Chem Int Ed"},{"issue":"15","key":"10.1016\/j.jvoice.2022.11.001_bib0060","first-page":"680","volume":"75","author":"Pontes","year":"2009","journal-title":"Transfer function of Brazilian Portuguese oral vowels: a comparative acoustic analysis"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0061","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/0010-4825(90)90013-F","article-title":"A note on the use of the intraclass correlation coefficient in the evaluation of agreement between two methods of measurement","volume":"20","author":"Bland","year":"1990","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0062","unstructured":"Florencio V de O, Almeida A, Voice PBJ. Differences and reliability of linear and nonlinear acoustic measures as a function of vocal intensity in individuals with voice disorders. Elsevier. 2021. Available at: https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0892199721001442?casa_token=bPaGKrqFaW4AAAAA:FRcA97bUvP-WKiV_QT8S4wGht6IJJFNQS15vmubgmMvlEiqakKkhUe13A_ug1NFw7M9Q3lyA. Accessed May 19, 2022."},{"issue":"8","key":"10.1016\/j.jvoice.2022.11.001_bib0063","doi-asserted-by":"crossref","first-page":"085709-1","DOI":"10.1063\/1.5024948","article-title":"Exploiting nonlinearity of the speech production system for voice disorder assessment by recurrence quantification analysis","volume":"28","author":"Vieira","year":"2018","journal-title":"Chaos"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0064","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1016\/j.jvoice.2018.09.004","article-title":"Effectiveness of recurrence quantification measures in discriminating subjects with and without voice disorders","volume":"34","author":"Lopes","year":"2018","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0065","unstructured":"Chris Albon. Machine learning with python cookbook practical solutions from preprocessing to deep learning. 2018:304. https:\/\/www.docdroid.net\/Z87gYoF\/machine-learning-with-python-cookbook-en-pdf"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0066","series-title":"Machine learning","first-page":"432p","author":"Mitchell","year":"1997"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2019.105524","article-title":"Investigating the impact of data normalization on classification performance","volume":"97","author":"Singh","year":"2020","journal-title":"Appl Soft Comput"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0071","doi-asserted-by":"crossref","first-page":"79","DOI":"10.2478\/rput-2019-0029","article-title":"Impact of data normalization on classification model accuracy","volume":"27","author":"Borkin","year":"2019","journal-title":"Research Papers Faculty of Materials Science and Technology Slovak University of Technology"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0072","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2019.105524","article-title":"Investigating the impact of data normalization on classification performance","volume":"97","author":"Singh","year":"2020","journal-title":"Appl Soft Comput"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0073","doi-asserted-by":"crossref","unstructured":"Kuhn M, Johnson K. Feature engineering and selection. feature engineering and selection: Boca Raton, Florida. 2020. http:\/\/www.feat.engineering\/77","DOI":"10.1201\/9781315108230"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0074","doi-asserted-by":"crossref","first-page":"947.e11","DOI":"10.1016\/j.jvoice.2018.07.014","article-title":"A survey on machine learning approaches for automatic detection of voice disorders","volume":"33","author":"Hegde","year":"2019","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0075","first-page":"359","article-title":"Predictive analytics and data mining","volume":"15","author":"Chauhan","year":"2015","journal-title":"Business Intelligence"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0076","doi-asserted-by":"crossref","unstructured":"Li J, Cheng K, Wang S, et al. Feature selection: a data perspective. Vol. 50, ACM computing surveys. Association for Computing Machinery; 2017.","DOI":"10.1145\/3136625"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0077","unstructured":"Steve Jadav. Voice-based gender identification using machine learning. https:\/\/ieeexplore.ieee.org\/xpl\/conhome\/8766336\/proceeding"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0078","doi-asserted-by":"crossref","first-page":"458","DOI":"10.2307\/2348743","article-title":"Applied Logistic Regression","volume":"40","author":"Iyer","year":"1991","journal-title":"The Statistician"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0079","doi-asserted-by":"crossref","first-page":"159","DOI":"10.2307\/2529310","article-title":"The measurement of observer agreement for categorical data","volume":"33","author":"Landis","year":"1977","journal-title":"Biometrics"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0080","article-title":"Voice feature selection to improve performance of machine learning models for voice production inversion","author":"Zhang","year":"2021","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0081","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1111\/j.1536-7150.1979.tb02828.x","article-title":"On the measurement of discrimination against women","volume":"38","author":"Davis","year":"1979","journal-title":"Am J Econ Sociol"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0082","doi-asserted-by":"crossref","first-page":"16246","DOI":"10.1109\/ACCESS.2018.2816338","article-title":"Voice disorder identification by using machine learning techniques","volume":"6","author":"Verde","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0083","article-title":"Performance of acoustic measures for the discrimination among healthy, rough, breathy, and strained voices using the feedforward neural network","author":"de Abreu","year":"2022","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0084","doi-asserted-by":"crossref","DOI":"10.1016\/j.jvoice.2020.07.008","article-title":"Performance of different acoustic measures to discriminate individuals with and without voice disorders","volume":"36","author":"Lopes","year":"2022","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0085","article-title":"Cepstral measures in the assessment of severity of voice disorders","author":"Lopes","year":"2019","journal-title":"SciELO Brasil"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0086","unstructured":"Verde L, Pietro G de. Voice disorder identification by using machine learning techniques. 2018. Available at: https:\/\/ieeexplore.ieee.org\/abstract\/document\/8316845\/. Accessed February 8, 2022."},{"key":"10.1016\/j.jvoice.2022.11.001_bib0087","series-title":"2020 3rd International Seminar on Research of Information Technology and Intelligent Systems, ISRITI 2020","first-page":"694","article-title":"Feature selection on magelang duck egg candling image using variance threshold method","author":"Siti Ambarwati","year":"2020"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0088","doi-asserted-by":"crossref","first-page":"947.e11","DOI":"10.1016\/j.jvoice.2018.07.014","article-title":"A survey on machine learning approaches for automatic detection of voice disorders","volume":"33","author":"Hegde","year":"2019","journal-title":"J Voice"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0089","first-page":"196","article-title":"M\u00e9todo de Aprendizagem de M\u00e1quina para Classifica\u00e7\u00e3o da intensidade do desvio vocal utilizando Random Forest","author":"Leite","year":"2020","journal-title":"J Health Inform"},{"key":"10.1016\/j.jvoice.2022.11.001_bib0093","doi-asserted-by":"crossref","first-page":"634","DOI":"10.1016\/j.jvoice.2018.02.003","article-title":"Detection of pathological voice using cepstrum vectors: a deep learning approach","volume":"33","author":"Fang","year":"2019","journal-title":"J Voice"}],"container-title":["Journal of Voice"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0892199722003538?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0892199722003538?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T19:44:46Z","timestamp":1775072686000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0892199722003538"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5]]},"references-count":87,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["S0892199722003538"],"URL":"https:\/\/doi.org\/10.1016\/j.jvoice.2022.11.001","relation":{},"ISSN":["0892-1997"],"issn-type":[{"value":"0892-1997","type":"print"}],"subject":[],"published":{"date-parts":[[2025,5]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Different Performances of Machine Learning Models to Classify Dysphonic and Non-Dysphonic Voices","name":"articletitle","label":"Article Title"},{"value":"Journal of Voice","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jvoice.2022.11.001","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 The Voice Foundation. Published by Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}]}}