ANN BASED EARLY DETECTION OF ALZHEIMER DISEASE ON SELECTED FEATURES

Year 2023, Volume: 11 Issue: 4, 1508 - 1516, 30.12.2023

Seyit Gazi Yıldız , Kazım Yıldız

https://doi.org/10.21923/jesd.1296283

Abstract

Alzheimer's Disease (AD) is a type of dementia, also called cognitive impairment. In cases where measures are not taken against the disease, it may result in a decrease in the quality of life of the person and result in very serious consequences. While it presents with neurological consequences such as decreased functions of thinking and memory, it may result in death in advanced cases. The fact that the treatment is not completely possible makes the place of early diagnosis and intervention important for AD. As a result of the researches carried out in the study, it was seen that there are many studies and scientific content within the framework of AD. A method for early diagnosis of the disease was evaluated by using an open source shared dataset, which includes some disease-specific values and demographic characteristics. By using Artificial Neural Networks (ANN) model, which is one of the machine learning methods, it is aimed to be useful for other studies to take precautions for early detection of the disease. With the ANN, which was classified as dementia and non-dementia individuals, Root Mean Square Error (RMSE) value 0.2302, Mean Absolute Error (MAE) value 0.1899 and accuracy rate of 98.5% was obtained.

Keywords

Alzheimer Disease, ANN, Artificial Neural Networks, Machine Learning, Mild Cognitive Impairment.

SEÇİLMİŞ ÖZELLİKLER ÜZERİNDEN ALZHEİMER HASTALIĞININ YSA TABANLI ERKEN TEŞHİSİ

Abstract

Alzheimer Hastalığı(AH), bilişsel bozukluk olarak da adlandırılan bir tür demans hastalığıdır. Hastalığa karşı önlem alınmadığı durumlarda kişinin yaşam kalitesinde düşüşlere sebep olurken çok ciddi sonuçlarla neticelenebilir. Hastalıkla birlikte kişide düşünme ve hafıza yetisini kullanma fonksiyonlarında azalma görülebilir. Nörolojik sonuçlarla karşımıza çıkabileceği gibi, ileri durumlarda ölümle sonuçlanabilir. Tedavinin tam anlamıyla mümkün olmaması, AH için erken teşhis ve müdahalenin yerini önemli kılıyor. Çalışmada yapılan araştırmalar sonucunda, AH çerçevesinde birçok çalışma ve bilimsel içeriğin olduğu görülmüştür. Hastalığa özgü bazı değerlerin ve demografik özelliklerin yer aldığı açık kaynak olarak paylaşılmış veri seti kullanılarak, hastalığın erken teşhisine yönelik bir yöntem değerlendirilmiştir. Makine öğrenmesi yöntemlerinden Yapay Sinir Ağları (YSA) modeli kullanılarak, hastalığın erken tespiti yönünde önlemlerin zamanında alınmasına yönelik yapılacak diğer çalışmalara yararlı olması hedeflenmiştir. Demans ve demans olmayan birey şeklinde sınıflandırması yapılan Kök Ortalama Kare Hatası (KOKH) değeri 0.2302, Ortalama Mutlak Hata (OMH) değeri 0.1899 ve %98.5 doğruluk oranı elde edilmiştir.

Keywords

Alzheimer Hastalığı, YSA, Yapay Sinir Ağları, Makine Öğrenmesi, Hafif Bilişsel Bozukluk.

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