Medical Informatics IV

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DOI :10.26650/B/T3.2024.40.007   IUP :10.26650/B/T3.2024.40.007    Full Text (PDF)

Early Detection of Alzheimer Disease with Machine Learning

Ahmet Okan Arık, Neslihan Uzun, Ural Verimli, Arzu Baloğlu, Burcu Bulut Okay, Ulvi Başpınar

Alzheimer’s disease (AD) is one of the most common neurodegenerative diseases in the world. AD, which causes irreversible damage to the brain, has adverse effects on human life, such as memory loss, the need for constant care, and the inability to perform daily activities. Although there is no cure, early diagnosis has benefits such as slowing down the progression of the disease, starting treatment, participating in clinical trial treatments, receiving psychological support, and preparing families for the future. The study aims to develop an early detection model with the help of machine learning. The proposed model uses digitized MRI images of the Cognitive Normal (CN), Mild Cognitive Impairment (MCI), and AD patients from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset for early detection of AD. Most studies focus only on distinguishing the CN and AD groups, but the proposed model also successfully detects the MCI group. In addition, the proposed model outperforms many AD early detection models in the literature.


Keywords: Artificial intelligence, healthcare, Alzheimer disease, machine learning, gradient boosting
DOI :10.26650/B/T3.2024.40.007   IUP :10.26650/B/T3.2024.40.007    Full Text (PDF)

Alzheimer Hastalığının Maki̇ne Öğrenmesi̇ ile Erken Tespi̇ti̇

Ahmet Okan Arık, Neslihan Uzun, Ural Verimli, Arzu Baloğlu, Burcu Bulut Okay, Ulvi Başpınar

Alzheimer hastalığı (AH), dünyadaki en yaygın nörodejeneratif hastalıklardan biridir. Beyinde geri dönüşü olmayan hasarlara neden olan AH, hafıza kaybı, sürekli bakıma ihtiyaç duyma, günlük aktivitelerini yapamama gibi insan yaşamı üzerinde olumsuz etkilere sahiptir. Kesin bir tedavisi olmamakla birlikte erken teşhisin hastalığın ilerlemesini yavaşlatma, tedaviye başlama, klinik deneme tedavilere katılma, psikolojik destek alma, aileleri geleceğe hazırlama gibi faydaları vardır. Çalışma, makine öğrenimi yardımıyla bir AH erken tespit modeli geliştirmeyi amaçlamaktadır. Önerilen model, AH’nın erken tespiti için Alzheimer Hastalığı Nörogörüntüleme inisiyatifi (AHNİ) veri setinden Bilişsel Normal (BN), Hafif Bilişsel Bozukluk (HBB) ve AH hastalarının sayısallaştırılmış MRI görüntülerini kullanır. Çoğu çalışma sadece BN ve AH gruplarını ayırmaya odaklanır ancak, önerilen model HBB grubunu da başarıyla tespit edilebilmektedir. Ayrıca, önerilen model literatürdeki birçok AH erken tespit modelinden daha iyi performans göstermektedir. 


Keywords: Yapay zekâ, sağlık, Alzheimer hastalığı, makine öğrenmesi, gradyan arttırma

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