Alzheimer Hastalığının Tespitinde Makine Öğrenmesi Algoritmalarının Karşılaştırılması

Year 2022, Issue: 42, 1 - 5, 31.10.2022

Evin Şahin Sadık

https://doi.org/10.31590/ejosat.1190938

Abstract

Alzheimer hastalığı, bireylerde bilişsel fonksiyon kaybı ve bilişsel gerilemeye neden olan nörodejeneratif bir rahatsızlıktır. Hastalığın erken evrede tespit edilmesi hastalığın yıkıcı etkilerini yavaşlatmak için önem arz etmektedir. Uzman doktorlara teşhis sürecinde yardımcı olabilecek otonom bir bilgisayarlı bir destek sisteminin kullanılması zamandan tasarruf sağlayarak insan hatasının azaltılmasına yardımcı olur. Bu nedenle, Alzheimer hastalığının erken teşhisi için makine öğrenmesi algoritmalarından yararlanılarak yüksek doğruluklu bir sınıflandırma çalışması hedeflenmiştir. Bu çalışma kapsamında, 24 adet sağlıklı ve 24 adet Alzheimer hastası gönüllüden alınan Elektroensefalogram (EEG) sinyalleri ile oluşturulmuş açık kaynak olarak sunulan bir veri setinden yararlanılmıştır. EEG sinyallerinin her bir kanalından spektral ve istatistiksel öznitelikler olmak üzere 28 öznitelik çıkartılmıştır. Çıkartılan öznitelikler, karar ağacı öznitelik önem algoritmasına uygulanmış ve Alzheimer bireyler ile sağlıklı bireyleri ayırt edebilecek en anlamlı 5 öznitelik belirlenmiştir. Belirlenen öznitelikler ile dört makine öğrenmesi algoritması eğitilmiştir. Eğitim için verilerin %70’i kullanılmış ve algoritmalar 10-kat çapraz doğrulama yöntemi ile eğitilmiştir. Daha önce algoritmaların görmediği, test için ayrılan veriler ile makine öğrenmesi algoritmaları test edildiğinde en yüksek doğruluk % 96.43 ile Gradient Boosting Sınıflandırıcısı (GBC) algoritması ile elde edilmiştir.

Keywords

Alzheimer, Öznitelik çıkarma, Öznitelik seçme, Gradyan artırmalı sınıflandırma, makine öğrenmesi

Comparison Of Machine Learning Algorithms In The Detection Of Alzheimer's Disease

Abstract

Alzheimer's disease is a neurodegenerative disorder that causes loss of cognitive function and cognitive decline in individuals. Detection of the disease at an early stage is important to slow down the devastating effects of the disease. The use of an autonomous computerized support system that can assist specialist physicians in the diagnostic process saves time and helps reduce human error. For this reason, a high-accuracy classification study was aimed at utilizing different machine learning algorithms for early diagnosis of Alzheimer's disease. Within the scope of this study, an open source data set created with Electroencephalogram (EEG) signals from 24 healthy and 24 Alzheimer's patient volunteers was used. 28 features, including spectral and statistical features, were extracted from each channel of the EEG signals. The extracted features were evaluated to the feature importance algorithm and the five most significant features that could distinguish between Alzheimer's individuals and healthy individuals were determined. Four machine learning algorithms are trained with the determined features. 70% of the data was used for training and the algorithms were trained with a 10-fold cross-validation method. When the four machine learning algorithms were tested with the data reserved for testing, which the algorithms had not seen before, the highest accuracy was obtained with the Gradient Boosting Classifier (GBC) algorithm with 96.43%.

Keywords

alzheimer, EEG, Feature Extraction, Gradient Boosing Classifier

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