Analiza podatkov pacientov z Alzheimerjevo boleznijo z metodami strojnega učenja

MURGIĆ, IGOR (author), Kukar, Matjaž (mentor)

Cilj diplomske naloge je analiza podatkov bolnikov z Alzheimerjevo boleznijo in uporaba napovednih modelov, zgrajenih z metodami strojnega učenja. Zbrane podatke smo analizirali in poiskali zakonitosti med atributi. Atribute podatkov smo predstavili v obliki neusmerjenega grafa. Z uporabo zgrajenih modelov smo med atributi poiskali najpomembnejše in zavrgli tiste, ki so povzročali prekomerno prileganje. Tako dobljene modele smo testirali s pomočjo prečnega preverjanja in dobili rezultate točnosti modela. Zgrajeni modeli in primerjave med njimi so pokazale izstopanja nekaterih atributov, ki bi nam z manj preiskavami omogočili enostavnejšo in hitrejšo postavitev diagnoze same bolezni. Izločanje preiskav za zdravnike ni smiselno, saj jim povedo marsikaj o stanju bolnika. Lahko pa bi prilagodili vrstni red preiskav in s tem hitreje postavili diagnozo.

• Language: Slovenian
• Keywords: strojno učenje, Alzheimerjeva bolezen, analiza podatkov, gručenje, klasifikacija, prečno preverjanje, odločitvena drevesa, neusmerjeni grafi
• Work type: Bachelor thesis/paper
• Organization: FRI - Faculty of Computer and Information Science
• Year: 2017
• PID: 20.500.12556/RUL-97351
• Publication date in RUL: 24.10.2017

MURGIĆ, IGOR, 2017, Analiza podatkov pacientov z Alzheimerjevo boleznijo z metodami strojnega učenja [online]. Bachelor’s thesis. [Accessed 24 March 2025]. Retrieved from: https://repozitorij.uni-lj.si/IzpisGradiva.php?lang=eng&id=97351

Secondary language

• Language: English
• Title: Analyzing Alzheimer's patients' data with machine learning methods

Abstract

The aim of the diploma thesis is to analyze the data concerning patients with Alzheimer’s disease and to use the predictive models constructed through machine learning methods. The collected data was analyzed and the laws between attributes were defined. The data attributes were presented in the form of an undirected graph. The most relevant attributes were determined using the constructed models, the attributes that caused overfitting were eliminated. The models thus obtained were tested through cross-validation and the accuracy of each model was calculated. The constructed models and the comparisons between them showed that certain attributes were more distinctive than others. These attributes would enable us to simplify and expedite the establishment of the diagnosis of the disease, conducting fewer tests. Doctors deem the elimination of certain tests unreasonable, though, since a lot of information on the patient’s condition can be deduced from them. We could, however, modify the sequence of the tests, which would lead to more rapid establishment of the diagnosis.

• Keywords: machine learning, Alzheimer's disease, data analysis, clustering, classfication, cross-validation, decision trees, undirected graphs