NÖROLOJİK GİZEMLERİ AYDINLATMAK: ALZHEİMER HASTALIĞININ ERKEN TESPİTİNDE YAPAY ÖĞRENME YAKLAŞIMLARI

Abstract

Yaşla birlikte prevalansı artan demans, birden fazla kognitif alanda bozulma ile seyreden ve sonunda günlük yaşamı etkileyen bir klinik sendromdur. Buna bağlı olarak da bağımsızlığın kaybı, engellilik, bakıma ihtiyaç duyma ve ölümle sonuçlanmaktadır. Tüm demans vakalarının %50-80’ini Alzheimer Hastalığı (AH) oluşturmakta, 65 yaşından sonra olguların görülme sıklığı her beş yılda bir ikiye katlanmaktadır. Bu kapsamda Alzheimer hastalığının erken tespiti sağlık verilerinin erişilebilirliği ve veri işleme maliyetlerinin azalmasıyla artık mümkün hale gelmektedir. Çalışmanın amacı farklı yapay öğrenme yöntemleri kullanarak hastaları Alzheimer ve sağlıklı olarak sınıflandırmaktır. Bu amaçla OASIS-2: Longitudinal MRI Data in Nondemented and Demented Older Adults veri seti kullanılmıştır. Çalışmanın Alzheimer demansını erken tespit etme potansiyeli taşıdığı düşünüldüğünden değiştirilebilir risk faktörleri üzerinde klinik karar destek sistemlerine katkıda bulunması beklenmektedir

Keywords

• Yapay Öğrenme
• Demans
• Alzheimer
• Klinik Karar Destek Sistemleri
• Artırılmış Zeka

UNLOCKING NEUROLOGICAL MYSTERIES: MACHINE LEARNING APPROACHES to EARLY DETECTION of ALZHEIMER'S DISEASE

Abstract

Dementia is a clinical illness that becomes more common as people get older. It is defined by a decline in cognitive abilities across several domains and eventually impacts everyday functioning. Consequently, this leads to a decline in autonomy, impairment, dependence on assistance, and ultimately, mortality. Alzheimer's disease (AD) is responsible for 50–80% of all occurrences of dementia, and its occurrence increases by a factor of five every five years beyond the age of 65. Given the availability of health data and the decrease in data processing costs, it is now feasible to detect Alzheimer's disease at an early stage. The objective of this study is to classify individuals as either Alzheimer's sufferers or healthy individuals by employing various machine learning techniques. The OASIS-2 dataset, which consists of longitudinal MRI data from both nondemented and demented older adults, was utilized for this study. Given its potential for early detection of Alzheimer's dementia, the study is anticipated to enhance clinical decision support systems pertaining to modifiable risk factors.

Keywords

• Machine Learning
• Dementia
• Alzheimer
• Clinical Decision Support Systems
• Augmented Intelligence

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