FILTER FEATURE SELECTION ANALYSIS TO DETERMINE THE CHARACTERISTICS OF DEMENTIA

Year 2021, Volume: 29 Issue: 1, 20 - 27, 30.04.2021

Savaş Okyay Nihat Adar

https://doi.org/10.31796/ogummf.768872

Abstract

Dementias are known as neuropsychiatric disorders. As getting old, the chance of coming down with a dementia disease increases. Two-dimensional sliced brain scans can be generated via magnetic resonance imaging. Three-dimensional measurements of regions can be reached from those scans. For the samples in the ADNI dataset, the brain features are extracted through operating the Freesurfer brain analyzing tool. Parametrizing those features and demographic information in learning algorithms can label an unknown sample as healthy or dementia. On the other hand, some of the features in the initial set may be less practical than others. In this research, the aim is to decrease the feature-size, not the feature-dimension, as a first step to determine the most distinctive dementia characteristics. To that end, a total of 2264 samples (471 AD, 428 lMCI, 669 eMCI, 696 healthy controls) are divided into two sets: 65% training set (1464 samples) and 35% test set (800 samples). Various filter feature selection algorithms are tested over different parameters together with multiple Bayesian-based and tree-based classifiers. Test performance accuracy rates up to 76.50% are analyzed in detail. Instead of processing the whole feature set, the overall performance tends to increase with correctly fewer attributes taken.

Keywords

Alzheimer’s disease, Dementia, Filter feature selection, ADNI, Freesurfer

Supporting Institution

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Project Number

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Thanks

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DEMANS ÖZELLİKLERİNİN BELİRLENMESİ İÇİN FİLTRE ÖZNİTELİK SEÇİM ANALİZİ

Abstract

Demans hastalıkları nöropsikiyatrik bozukluklar olarak tanımlanır. Yaşlandıkça bir demans hastalığına yakalanma şansı da artış göstermektedir. Manyetik rezonans görüntüleme teknikleri ile iki boyutlu dilimlenmiş beyin taramaları oluşturulabilir. Bu taramalar üzerinden bölgelerin üç boyutlu ölçümlerine ulaşılabilir. ADNI veri setindeki örnekler için, beyin özellikleri Freesurfer beyin analiz aracı kullanılarak çıkarılmaktadır. Bu özelliklerin ve demografik verinin öğrenme algoritmalarında parametreler olarak yer almasıyla, bilinmeyen bir örnek, sağlıklı veya demans olarak etiketlenebilir. Öte yandan, tüm özellik setindeki bazı öznitelikler diğerlerine göre daha az yararlı veya direkt etkisiz olabilir. Bu araştırmanın amacı, en belirgin demans özelliklerini belirlemek adına ilk adım olarak öznitelik sayısını azaltmaktır. Bu amaçla, toplam 2264 numune (471 AH, 428 gHBB, 669 eHBB, 696 sağlıklı kontrol), % 65 eğitim seti (1464 numune) ve % 35 test seti (800 numune) olmak üzere iki gruba ayrılmaktadır. Çeşitli filtre öznitelik seçim algoritmaları, Bayes tabanlı ve ağaç tabanlı sınıflandırıcılarla birlikte farklı parametreler üzerinden test edilmektedir. % 76,50'ye varan test performans doğruluğu oranları ayrıntılı olarak analiz edilmektedir. Öznitelik setinin tamamını işlemek yerine, doğru şekilde daha az öznitelik alındığında genel performans artış eğilimindedir.

Keywords

Alzheimer hastalığı, Demans, Filtre öznitelik seçimi, ADNI, Freesurfer

Project Number

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