EMPOWERING SELF-DETECTION: A GRAPHICAL USER INTERFACE POWERED BY MACHINE LEARNING FOR EARLY DIAGNOSIS OF ALZHEIMER'S DISEASE

Öz

Alzheimer’s Disease (AD) is one of the most, if not the most, devastating neurodegenerative diseases that are incurable and progressive. Early diagnosis of AD comes with many promises in terms of medicine, sociology, and economics. Despite the existence of numerous studies that aim for early diagnosis of AD, to the best of our knowledge, there is not a publicly available tool that lets end-users assess AD. To address this gap, we propose a Graphical User Interface (GUI) powered by Machine Learning (ML) that makes self-assessment of AD possible – without any input from medical experts. The developed GUI lets end-users enter various information considering both commonly used features for the diagnosis of AD and the questions available in the gold standard screening tool for the diagnosis of AD, namely the Mini-Mental State Exam. In addition to employing 11 traditional ML algorithms, we propose a novel 1-dimensional (1D) Convolutional Neural Network (CNN). All ML models were trained on a gold standard dataset that comprised 373 records from three subject classes as follows: (i) non-demented, (ii) demented, and (iii) converted. Once the end-user enters the required input through the developed GUI, the previously trained ML model assesses the diagnosis of AD through this input in a couple of seconds. According to the experimental results, the proposed novel 1D CNN outperformed the state-of-the-art by obtaining an accuracy as high as 95,3% on the used gold standard dataset.

Anahtar Kelimeler

• Alzheimer’s Disease
• dementia
• cognitive disorder
• deep learning
• machine learning
• classification

KENDİNE TANIMANIN GÜÇLENDİRİLMESİ: ALZHEİMER HASTALIĞININ ERKEN TANISINA YÖNELİK MAKİNE ÖĞRENMESİ TEMELLİ BİR GRAFİKSEL KULLANICI ARABİRİMİ

Öz

Alzheimer Hastalığı (AH), tedavi edilemeyen ve ilerleyici olan en yıkıcı nörodejeneratif hastalıklardan biridir, belki de en yıkıcı olanıdır. AH'nin erken teşhisi, tıp, sosyoloji ve ekonomi açısından birçok avantaj içermektedir. AH'nin erken teşhisine yönelik birçok çalışma olmasına rağmen, bilgimiz dahilinde olan son kullanıcıların AH değerlendirmesini yapmalarına olanak tanıyan açık erişimli bir araç bulunmamaktadır. Bu boşluğu doldurmak için, tıbbi uzmanlardan herhangi bir giriş olmadan AH'nin kendi değerlendirmesini mümkün kılan Makine Öğrenmesi temelli bir grafiksel kullanıcı arayüzü öneriyoruz. Geliştirilen grafiksel kullanıcı arayüzü, son kullanıcılara AH teşhisi için yaygın olarak kullanılan özniteliklerle birlikte AH teşhisi için altın standart tarama aracı olan Mini-Mental Durum Testi’ndeki soruları da dikkate alarak çeşitli bilgiler girmelerine izin verir. 11 geleneksel makine öğrenmesi algoritmasının kullanımının yanı sıra, benzersiz bir 1-boyutlu Konvolüsyonel Sinir Ağı (KSA) öneriyoruz. Tüm makine öğrenmesi modelleri, (i) bilişsel bozukluğu olmayan, (ii) bilişsel bozukluğu olan ve (iii) dönüştürülen olmak üzere üç konu sınıfından oluşan 373 örneklemli bir altın standart veri setinde eğitilmiştir. Son kullanıcı, geliştirilen grafiksel kullanıcı arayüzü aracılığıyla gerekli girişi yaptığında, daha önce eğitilmiş makine öğrenmesi modeli bu girdi üzerinden AH teşhisini birkaç saniye içinde değerlendirmektedir. Deneysel sonuçlara göre, önerilen benzersiz 1-boyutlu KSA, kullanılan altın standart veri setinde %95,3'e kadar yüksek bir doğruluk elde ederek en gelişkin modelleri geride bırakmıştır.

Anahtar Kelimeler

• Alzheimer Hastalığı
• demans
• bilişsel bozukluk
• derin öğrenme
• makine öğrenmesi
• sınıflandırma

Kaynakça

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