Napovedovanje parkinsonove bolezni z analizo govora s pametnim telefonom

ZUPANC, ANDREJ (author), Bratko, Ivan (mentor)

Zgodnja diagnoza bolezni lahko precej vpliva na njen nadaljnji potek, pravo\-časno zdravljenje in kvaliteto življenja. Vendar vse bolezni niso ozdravljive, zdravniki lahko pomagajo le lajšati simptome. Takšna je parkinsonova bolezen, ki je nevrodegenerativna bolezen, katere glavni simptomi so tremor v mirovanju, počasno začenjanje gibov, mišična rigidnost in tudi težave z govorom. Zato smo se v tej diplomski nalogi odločili razviti sistem za zgodnje odkrivanje parkinsonove bolezni, ki bi znal prepoznati znake parkinsonove bolezni v govoru. V ta namen smo razvili mobilno aplikacijo, vmesnik API in klasifikator. Vmesnik API shrani zvočne posnetke, posnete z mobilno aplikacijo, jih analizira in klasificira s klasifikatorjem, ki je bil prav tako razvit v sklopu diplomske naloge. Po končani klasifikaciji vmesnik API vrne rezultat mobilni aplikaciji, ki uporabnika obvesti o rezultatu analize njegovega glasu. Aplikacija je bila razvita za operacijski sistem Android. Vmesnik API je razvit s pomočjo knjižnice Flask. Različice klasifikatorjev so bile razvite s knjižnico Scikit learn in Keras, med katerimi smo izbrali najboljšega in ga implementirali v vmesnik API. Predstavljen je tudi primer uporabe tega izdelka.

• Language: Slovenian
• Keywords: strojno učenje, parkinsonova bolezen, mobilna aplikacija, neuravnoteženi podatki
• Work type: Bachelor thesis/paper
• Organization: FRI - Faculty of Computer and Information Science
• Year: 2017
• PID: 20.500.12556/RUL-91028
• Publication date in RUL: 10.03.2017

ZUPANC, ANDREJ, 2017, Napovedovanje parkinsonove bolezni z analizo govora s pametnim telefonom [online]. Bachelor’s thesis. [Accessed 31 March 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=91028

Secondary language

• Language: English
• Title: Predicting Parkinson's Disease with Voice Analysis on a Smartphone

Abstract

Early diagnosis can have significant effect on disease progression, its treatment and patient's quality of life. However, some diseases are incurable, and doctors can only help relieve symptoms. One of such is Parkinson's disease, a neurodegenerative disease marked by tremor, slowness of movement, muscular rigidity and difficulty with speaking. The aim of this paper was to develop a system for early diagnosis of Parkinson's disease which could recognize signs of Parkinson's disease in a person's voice. For this purpose, a mobile application, an API interface and a classifier were developed. The API interface saves voice recordings made by the mobile application, then analyses and classifies them with the classifier. After the classification is done, the API interface sends the result back to the mobile application which informs its user about the outcome of their voice analysis. The application was developed for Android operating system. The API interface is based on the Flask library. Different classifiers using libraries Scikit-learn and Keras were developed. Then, the most appropriate classifier was chosen and implemented into the API interface. An example of how the application can be used is also described.

• Keywords: machine learning, Parkinson's disease, mobile application, imbalanced data