Investigation of The Risk of Diabetes in Early Period using Machine Learning Algorithms

Öz

Diabetes significantly affecting the quality of human life, the world and the incidence of a disease in Turkey is increasingly important. In particular, it causes damage to the nervous system, kidney, heart, eyes, limbs and blood vessels and can cause significant losses. For this reason, early diagnosis and follow-up is of great importance in order to prevent diabetes or to minimize the damage it will cause. Classification techniques obtained by machine learning algorithms have been accepted as important by researchers for the risk prediction model of the disease. In the study, a database created with information from 520 subjects was used to estimate the probability of developing diabetes. In the study, Multilayer Perceptron Artificial Neural Networks (MLPNN), Support Vector Machines (SVM), Decision Trees (DT), Ensemble Learning Algorithms (ELA), Linear Discriminant Analysis (LDA), k-NN Methods were used as machine learning methods. Among these methods, k-NN algorithm provided the highest accuracy and 99,81% accuracy was achieved with this algorithm. A diabetes early diagnosis kit was developed by including the algorithm providing the highest accuracy value into a computer user interface developed within the scope of the study

Anahtar Kelimeler

• Diabetes risk
• Early stage
• Support Vector Machine
• Ensembles Learning
• Algorithm
• Decision Trees

Makine Öğrenmesi Algoritmaları Kullanarak Erken Dönemde Diyabet Hastalığı Riskinin Araştırılması

Öz

Diyabet insanoğlunun yaşam kalitesini önemli derecede etkileyen, dünyada ve Türkiye’de görülme sıklığı giderek artan önemli bir hastalıktır. Özellikle sinir sistemi, böbrek, kalp, gözler, uzuvlar ve kan damarlarının tahribatına yol açmakta ve önemli kayıplara sebebiyet verebilmektedir. Bu sebeple diyabetin önlenebilmesi veya vereceği tahribatın en aza indirilebilmesi için erken tanısı ve takibi büyük önem kazanmaktadır. Makine öğrenme algoritmaları ile elde edilen sınıflandırma teknikleri, hastalığın risk tahmin modeli için araştırmacılar tarafından önemli olarak kabul görmüştür. Çalışmada, diyabete yakalanma olasılığını tahmin etmek için, 520 denekten alınan bilgiler ile oluşturulmuş olan bir veri tabanı kullanılmıştır. Çalışmada, makine öğrenmesi metotları olarak Çok Katmanlı Algılayıcı Yapay Sinir Ağları (ÇKAYSA), Destek Vektör Makinaları (DVM), Karar Ağaçları (KA), Topluluk Öğrenme Algoritmaları (TÖA), Doğrusal Ayrımcı Analizi (DAA), k-NN Metotları kullanılmıştır. Bu metotlar arasında en yüksek doğruluğu k-NN algoritması sağlamış ve bu algoritma ile %99,81 doğruluk elde edilmiştir. En yüksek doğruluk değeri sağlayan algoritmanın çalışma kapsamında geliştirilmiş olan bir bilgisayar kullanıcı arayüzü içerisine dâhil edilmesiyle bir diyabet erken tanı kiti geliştirilmiştir.

Anahtar Kelimeler

• Diyabet riski
• Erken dönem
• Destek Vektör Makinesi
• Topluluk Öğrenme Algoritmaları
• Karar ağaçları

Kaynakça

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