{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T02:25:45Z","timestamp":1758248745822,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T00:00:00Z","timestamp":1700697600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,11,23]]},"DOI":"10.1145\/3647444.3647926","type":"proceedings-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T13:48:36Z","timestamp":1715608116000},"page":"1-8","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Early diagnosis of Parkinson disease using Machine Learning Techniques"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8974-3147","authenticated-orcid":false,"given":"Atul","family":"Srivastava","sequence":"first","affiliation":[{"name":"Computer Science and Engineering, Amity University Uttar Pradesh, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4536-9445","authenticated-orcid":false,"given":"Harshita","family":"Rana","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering, Amity University Uttar Pradesh, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8351-1724","authenticated-orcid":false,"given":"Prashant","family":"Dixit","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Galgotias University, India"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-1695-1606","authenticated-orcid":false,"given":"Reecha","family":"Singh","sequence":"additional","affiliation":[{"name":"UIM Department, Uttaranchal university, India"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"volume-title":"In2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel 2012 Nov 14 (pp. 1-4). IEEE.","author":"Hazan H","key":"e_1_3_2_1_1_1","unstructured":"Hazan H, Hilu D, Manevitz L, Ramig LO, Sapir S. Early diagnosis of Parkinson's disease via machine learning on speech data. In2012 IEEE 27th Convention of Electrical and Electronics Engineers in Israel 2012 Nov 14 (pp. 1-4). IEEE."},{"volume-title":"In2014 IEEE international conference on software science, technology and engineering 2014 Jun 11 (pp. 50-53)","author":"Frid A","key":"e_1_3_2_1_2_1","unstructured":"Frid A, Safra EJ, Hazan H, Lokey LL, Hilu D, Manevitz L, Ramig LO, Sapir S. Computational diagnosis of Parkinson's disease directly from natural speech using machine learning techniques. In2014 IEEE international conference on software science, technology and engineering 2014 Jun 11 (pp. 50-53). IEEE."},{"volume-title":"In2017 IEEE MIT Undergraduate Research Technology Conference (URTC) 2017 Nov 3 (pp. 1-4). IEEE.","author":"Dinesh A","key":"e_1_3_2_1_3_1","unstructured":"Dinesh A, He J. Using machine learning to diagnose Parkinson's disease from voice recordings. In2017 IEEE MIT Undergraduate Research Technology Conference (URTC) 2017 Nov 3 (pp. 1-4). IEEE."},{"volume-title":"Parkinson's disease diagnosis using machine learning and voice. In2018 IEEE signal processing in medicine and biology symposium (SPMB) 2018 Dec 1 (pp. 1-7)","author":"Wroge TJ","key":"e_1_3_2_1_4_1","unstructured":"Wroge TJ, \u00d6zkanca Y, Demiroglu C, Si D, Atkins DC, Ghomi RH. Parkinson's disease diagnosis using machine learning and voice. In2018 IEEE signal processing in medicine and biology symposium (SPMB) 2018 Dec 1 (pp. 1-7). IEEE."},{"volume-title":"In2018 IEEE International Symposium on Multimedia (ISM) 2018 (pp. 271-274)","author":"Barbon Junior S","key":"e_1_3_2_1_5_1","unstructured":"Barbon Junior S, Turrisi da Costa VG, Chen SH, Guido RC. U-healthcare system for pre-diagnosis of Parkinson's disease from voice signal. In2018 IEEE International Symposium on Multimedia (ISM) 2018 (pp. 271-274). IEEE."},{"key":"e_1_3_2_1_6_1","first-page":"37718","volume":"2","author":"Haq AU","unstructured":"Haq AU, Li JP, Memon MH, Malik A, Ahmad T, Ali A, Nazir S, Ahad I, Shahid M. Feature selection based on L1-norm support vector machine and effective recognition system for Parkinson's disease using voice recordings. IEEE access. 2019 Mar 21;7:37718-34.","journal-title":"Mar"},{"volume-title":"Machine Learning Approach. In2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) 2021 Aug 27 (pp. 869-872)","author":"Vigneswari DA","key":"e_1_3_2_1_7_1","unstructured":"Vigneswari DA, Aravinth J. Parkinson's disease Diagnosis using Voice Signals by Machine Learning Approach. In2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) 2021 Aug 27 (pp. 869-872). IEEE."},{"key":"e_1_3_2_1_8_1","volume-title":"The detection of Parkinson's disease from speech using voice source information","author":"Narendra NP","year":"2021","unstructured":"Narendra NP, Schuller B, Alku P. The detection of Parkinson's disease from speech using voice source information. IEEE\/ACM Transactions on Audio, Speech, and Language Processing. 2021 May 7;29:1925-36."},{"volume-title":"In2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) 2022 Apr 7 (pp. 200-206)","author":"Rao DV","key":"e_1_3_2_1_9_1","unstructured":"Rao DV, Sucharitha Y, Venkatesh D, Mahamthy K, Yasin SM. Diagnosis of parkinson's disease using principal component analysis and machine learning algorithms with vocal features. In2022 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) 2022 Apr 7 (pp. 200-206). IEEE."},{"volume-title":"In2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) 2022 Apr 28 (pp. 1436-1439)","author":"Hussain A","key":"e_1_3_2_1_10_1","unstructured":"Hussain A, Sharma A. Machine Learning Techniques for Voice-based Early Detection of Parkinson's Disease. In2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) 2022 Apr 28 (pp. 1436-1439). IEEE."},{"key":"e_1_3_2_1_11_1","first-page":"97600","volume":"2022","author":"Motin MA","unstructured":"Motin MA, Pah ND, Raghav S, Kumar DK. Parkinson's Disease Detection Using Smartphone Recorded Phonemes in Real World Conditions. IEEE Access. 2022 Sep 12;10:97600-9.","journal-title":"Real World Conditions. IEEE Access."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"Cortes C Vapnik V. Support-vector networks. Machine learning. 1995 Sep;20:273-97.","DOI":"10.1023\/A:1022627411411"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez\u2010Mart\u00edn P Gil\u2010Nagel A Gracia LM G\u00f3mez JB Martinez\u2010Sarries J Bermejo F Cooperative Multicentric Group. Unified Parkinson's disease rating scale characteristics and structure. Movement disorders. 1994;9(1):76-83.","DOI":"10.1002\/mds.870090112"},{"key":"e_1_3_2_1_14_1","volume-title":"Voice and articulation drillbook (2ndedn) New York","author":"Fairbanks G.","year":"1960","unstructured":"Fairbanks G. Voice and articulation drillbook (2ndedn) New York. NY: Harper & Row. 1960."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"Chang CC Lin CJ. LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST). 2011 May 6;2(3):1-27.","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_3_2_1_16_1","unstructured":"Fan RE Chen PH Lin CJ Joachims T. Working set selection using second order information for training support vector machines. Journal of machine learning research. 2005 Dec 1;6(12)."},{"key":"e_1_3_2_1_17_1","first-page":"4","volume":"2021","author":"Pahuja G","unstructured":"Pahuja G, Nagabhushan TN. A comparative study of existing machine learning approaches for Parkinson's disease detection. IETE Journal of Research. 2021 Jan 2;67(1):4-14.","journal-title":"Research."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"crossref","unstructured":"Mei J Desrosiers C Frasnelli J. Machine learning for the diagnosis of Parkinson's disease: a review of literature. Frontiers in aging neuroscience. 2021 May 6;13:633752.","DOI":"10.3389\/fnagi.2021.633752"},{"key":"e_1_3_2_1_19_1","first-page":"1449","volume":"2005","author":"Huse DM","unstructured":"Huse DM, Schulman K, Orsini L, Castelli\u2010Haley J, Kennedy S, Lenhart G. Burden of illness in Parkinson's disease. Movement disorders: official journal of the Movement Disorder Society. 2005 Nov;20(11):1449-54.","journal-title":"Movement Disorder Society."},{"key":"e_1_3_2_1_20_1","unstructured":"Dataset: https:\/\/archive.ics.uci.edu\/dataset\/174\/parkinsons"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Bot BM Suver C Neto EC Kellen M Klein A Bare C Doerr M Pratap A Wilbanks J Dorsey E Friend SH. The mPower study Parkinson disease mobile data collected using ResearchKit. Scientific data. 2016 Mar 3;3(1):1-9.","DOI":"10.1038\/sdata.2016.11"},{"volume-title":"InProceedings of the 21st ACM international conference on Multimedia 2013 Oct 21 (pp. 835-838)","author":"Eyben F","key":"e_1_3_2_1_22_1","unstructured":"Eyben F, Weninger F, Gross F, Schuller B. Recent developments in opensmile, the munich open-source multimedia feature extractor. InProceedings of the 21st ACM international conference on Multimedia 2013 Oct 21 (pp. 835-838)."},{"key":"e_1_3_2_1_23_1","first-page":"185","volume":"2017","author":"B\u00e4ckstr\u00f6m T","unstructured":"B\u00e4ckstr\u00f6m T, Christian Uhle, B\u00e4ckstr\u00f6m T. Voice activity detection. Speech Coding: Code-Excited Linear Prediction. 2017:185-203.","journal-title":"Code-Excited Linear Prediction."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511801389"},{"key":"e_1_3_2_1_25_1","first-page":"101","volume":"14","author":"Haq AU","unstructured":"Haq AU, Li J, Memon MH, Khan J, Din SU, Ahad I, Sun R, Lai Z. Comparative analysis of the classification performance of machine learning classifiers and deep neural network classifier for prediction of Parkinson disease. In2018 15th international computer conference on wavelet active media technology and information processing (ICCWAMTIP) 2018 Dec 14 (pp. 101-106). IEEE.","journal-title":"Dec"},{"key":"e_1_3_2_1_26_1","first-page":"47","volume":"2019","author":"Narendra NP","unstructured":"Narendra NP, Alku P. Dysarthric speech classification from coded telephone speech using glottal features. Speech Communication. 2019 Jul 1;110:47-55.","journal-title":"Speech Communication."},{"key":"e_1_3_2_1_27_1","volume-title":"Quasi closed phase glottal inverse filtering analysis with weighted linear prediction","author":"Airaksinen M","year":"2013","unstructured":"Airaksinen M, Raitio T, Story B, Alku P. Quasi closed phase glottal inverse filtering analysis with weighted linear prediction. IEEE\/ACM Transactions on Audio, Speech, and Language Processing. 2013 Dec 11;22(3):596-607."},{"key":"e_1_3_2_1_28_1","first-page":"342","volume":"2014","author":"Orozco-Arroyave JR","unstructured":"Orozco-Arroyave JR, Arias-Londo\u00f1o JD, Vargas-Bonilla JF, Gonzalez-R\u00e1tiva MC, N\u00f6th E. New Spanish speech corpus database for the analysis of people suffering from Parkinson's disease. InLREC 2014 May (pp. 342-347).","journal-title":"InLREC"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Alku P. Glottal wave analysis with pitch synchronous iterative adaptive inverse filtering. Speech communication. 1992 Jun 1;11(2-3):109-18.","DOI":"10.1016\/0167-6393(92)90005-R"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Zhang HH Yang L Liu Y Wang P Yin J Li Y Qiu M Zhu X Yan F. Classification of Parkinson's disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples. Biomedical engineering online. 2016 Dec;15:1-22.","DOI":"10.1186\/s12938-016-0242-6"},{"key":"e_1_3_2_1_31_1","volume-title":"Principal component analysis","author":"Abdi H","year":"2010","unstructured":"Abdi H, Williams LJ. Principal component analysis. Wiley interdisciplinary reviews: computational statistics. 2010 Jul;2(4):433-59."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"Breiman L. Random forests. Machine learning. 2001 Oct;45:5-32.","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_3_2_1_33_1","first-page":"364","volume":"2013","author":"Zuo WL","unstructured":"Zuo WL, Wang ZY, Liu T, Chen HL. Effective detection of Parkinson's disease using an adaptive fuzzy k-nearest neighbor approach. Biomedical Signal Processing and Control. 2013 Jul 1;8(4):364-73.","journal-title":"Control."},{"volume-title":"In2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP) 2018 Aug 21 (pp. 255-258)","author":"Pavlyshenko B.","key":"e_1_3_2_1_34_1","unstructured":"Pavlyshenko B. Using stacking approaches for machine learning models. In2018 IEEE Second International Conference on Data Stream Mining & Processing (DSMP) 2018 Aug 21 (pp. 255-258). IEEE."},{"key":"e_1_3_2_1_35_1","first-page":"1495","volume":"2","author":"Rusz J","unstructured":"Rusz J, Hlavni\u010dka J, Tykalova T, Novotn\u00fd M, Du\u0161ek P, \u0160onka K, R\u016f\u017ei\u010dka E. Smartphone allows capture of speech abnormalities associated with high risk of developing Parkinson's disease. IEEE transactions on neural systems and rehabilitation engineering. 2018 Jun 29;26(8):1495-507.","journal-title":"Jun"},{"key":"e_1_3_2_1_36_1","first-page":"1","volume":"2011","author":"Ogden R.","unstructured":"Ogden R. An introduction to English phonetics. Phonetica. 2011 Jul 1;68(1-2):111-2.","journal-title":"Phonetica."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Levine CB Fahrbach KR Siderowf AD Estok RP Ludensky VM Ross SD. Diagnosis and treatment of Parkinson's disease: a systematic review of the literature. Evidence report\/technology assessment (Summary). 2003 May 1(57):1-4.","DOI":"10.1037\/e439422005-001"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"Sapir S Ramig L Fox C. Speech and swallowing disorders in Parkinson disease. Current opinion in otolaryngology & head and neck surgery. 2008 Jun 1;16(3):205-10.","DOI":"10.1097\/MOO.0b013e3282febd3a"},{"key":"e_1_3_2_1_39_1","first-page":"815","volume":"2011","author":"Sapir S","unstructured":"Sapir S, Ramig LO, Fox CM. Intensive voice treatment in Parkinson's disease: Lee Silverman voice treatment. Expert Review of Neurotherapeutics. 2011 Jun 1;11(6):815-30.","journal-title":"Neurotherapeutics."},{"key":"e_1_3_2_1_40_1","first-page":"350","volume":"2011","author":"Rusz J","unstructured":"Rusz J, Cmejla R, Ruzickova H, Ruzicka E. Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkinson's disease. The journal of the Acoustical Society of America. 2011 Jan 1;129(1):350-67.","journal-title":"America."},{"key":"e_1_3_2_1_41_1","unstructured":"Sapir S Spielman JL Ramig LO Story BH Fox C. Effects of intensive voice treatment (the Lee Silverman Voice Treatment [LSVT]) on vowel articulation in dysarthric individuals with idiopathic Parkinson disease: acoustic and perceptual findings."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"Little M McSharry P Hunter E Spielman J Ramig L. Suitability of dysphonia measurements for telemonitoring of Parkinson's disease. Nature Precedings. 2008 Sep 12:1-.","DOI":"10.1038\/npre.2008.2298.1"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Harel B Cannizzaro M Snyder PJ. Variability in fundamental frequency during speech in prodromal and incipient Parkinson's disease: A longitudinal case study. Brain and cognition. 2004 Oct 1;56(1):24-9.","DOI":"10.1016\/j.bandc.2004.05.002"},{"key":"e_1_3_2_1_44_1","first-page":"361","volume":"2019","author":"Rayan Z","unstructured":"Rayan Z, Alfonse M, Salem AB. Machine learning approaches in smart health. Procedia Computer Science. 2019 Jan 1;154:361-8.","journal-title":"Procedia Computer Science."},{"key":"e_1_3_2_1_45_1","first-page":"55","volume":"2019","author":"Almeida JS","unstructured":"Almeida JS, Rebou\u00e7as Filho PP, Carneiro T, Wei W, Dama\u0161evi\u010dius R, Maskeli\u016bnas R, de Albuquerque VH. Detecting Parkinson's disease with sustained phonation and speech signals using machine learning techniques. Pattern Recognition Letters. 2019 Jul 1;125:55-62.","journal-title":"Pattern Recognition Letters."},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"crossref","unstructured":"Erdogdu Sakar B Serbes G Sakar CO. Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson's disease. PloS one. 2017 Aug 9;12(8):e0182428.","DOI":"10.1371\/journal.pone.0182428"},{"key":"e_1_3_2_1_47_1","volume-title":"InProceedings of the 3rd ACM international workshop on Audio\/visual emotion challenge 2013 Oct 21 (pp. 3-10)","author":"Valstar M","year":"2013","unstructured":"Valstar M, Schuller B, Smith K, Eyben F, Jiang B, Bilakhia S, Schnieder S, Cowie R, Pantic M. Avec 2013: the continuous audio\/visual emotion and depression recognition challenge. InProceedings of the 3rd ACM international workshop on Audio\/visual emotion challenge 2013 Oct 21 (pp. 3-10)."}],"event":{"name":"ICIMMI 2023: International Conference on Information Management & Machine Intelligence","acronym":"ICIMMI 2023","location":"Jaipur India"},"container-title":["Proceedings of the 5th International Conference on Information Management & Machine Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3647444.3647926","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3647444.3647926","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T17:21:10Z","timestamp":1758216070000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3647444.3647926"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,23]]},"references-count":47,"alternative-id":["10.1145\/3647444.3647926","10.1145\/3647444"],"URL":"https:\/\/doi.org\/10.1145\/3647444.3647926","relation":{},"subject":[],"published":{"date-parts":[[2023,11,23]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}