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Alzheimer's Disease Stage Prediction using Machine Learning and Multi Agent System
Blekinge Institute of Technology, School of Computing.
Blekinge Institute of Technology, School of Computing.
2012 (English)Independent thesis Advanced level (degree of Master (Two Years))Student thesisAlternative title
Alzheimers sjukdom Stage Prediction med Maskininlärning och Multi Agent System (Swedish)
Abstract [en]

Context : Alzheimer's disease is a memory impairment disease which mostly affects elderly people. Currently, about 4 million Americans and 5 million Europeans are affected by this disease. The occurrence of Alzheimer's disease is expected to quadruple by the year 2020. Alzheimer's disease cannot be cured or stopped its progression rather delay its progression. Early diagnosis of the disease helps the patients, the caregivers and health institutions to save time, cost and minimize patients suffering. Objectives : In this thesis, different machine learning algorithms used for classification purpose are evaluated and various Alzheimer's disease diagnosis techniques are identified. Among these algorithms, a suitable classifier that has better classification accuracy on the National Alzheimer Coordinating Center (NACC) dataset is selected. This classifier is customized in order to make it compatible for the NACC dataset and to receive the new instance from the user. Then a multi-agent system model is develop that can improve the classification accuracy. Methods : Different research works are reviewed and experiments are conducted throughout this research work. A dataset for this research is obtained from National Alzheimer's Coordinating Center, university of Washington. Using this dataset, two experiments are conducted in WEKA. In the first experiment, the five candidate algorithms are compared to select the significant classifier for medical history and cognitive function data. For the second experiment, two datasets are used; a dataset contains Medical History (MH) with Cognitive Function (CF) data and a dataset that contains only medical history data to check in which dataset the selected classifier has better accuracy. Results : From the first experiment, J48 classifier has a better stage prediction accuracy than the candidate algorithms with 61.12%. J48 is customized to classify a new instance received from the user and to improve the classification accuracy. Then the accuracy increase to 87.09% when the classifier's parameters are optimized. When the medical history and cognitive function data is experimented in WEKA separately, the classification accuracies of J48 on MH, CF and their combination datasets are 81.42%, 64.20% and 87.09% respectively. The agents simulation result showed that some misclassified instances by J48 algorithm can be corrected by using multi agent system. The experimental results are presented in graphical format. Conclusions : Hence we conclude that machine learning and agent system in combination can be used for Alzheimer's disease diagnosis and its stage prediction by extracting knowledge from a dataset which contains patients medical history and cognitive function data.

Abstract [sv]

Syftet med detta examensarbeta var att diagnostiserar Alzheimer patienter använder mönstret från en samling av andra tidigare diagnoserad patienter information och diagnosdata. Examensarbete hade tre huvuduppgifter: Förberedelse av data (mer än 10000 patienter data) för forskningen, maskininlärning algoritmer utvärderade med WEKA verktyg för att välja den bästa algoritmen och förbättra noggrannheten av den valda algoritmen med hjälp av agent system tekniker . - SQL queries används på uppgifter förberedelsefas. - WEKA programvara används för algoritmer utvärdering. - Agent arkitektur är utvecklat för att förbättra förutsäga av noggrannhets. Det bidrag av detta examensarbeta är identifiera Alzheimer patienter diagnos metod som använder en samling av patienternas diagnos information

Place, publisher, year, edition, pages
2012. , 63 p.
Keyword [en]
Alzheimer's disease stage prediction, classification, multi agent system, machine learning
National Category
Nursing Computer Science Production Engineering, Human Work Science and Ergonomics
URN: urn:nbn:se:bth-1985Local ID: diva2:829245
Note, them22dayz@gmail.comAvailable from: 2015-04-22 Created: 2013-02-18 Last updated: 2015-06-30Bibliographically approved

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