Bora TAŞTEKİN, Turgay İBRİKÇİ, Aykut PELİT, Pelin ÖZALP

PCA Destekli J48 Algoritması İle Manyetik Alanla Tedavi Edilen Diyabetik Sıçanların Biyokimyasal ve Biyomekanik Verilerinin Sınıflndırılması

Bu çalışmanın amacı, Wistar albino türü sıçanların diyabetik biyokimyasal değerleri ve Manyetik Alan Uygulamasıyla kasılma değerleriyle daha karmaşık hale getirilen veri setinin, istatistiksel algoritmalarından biri olan temel bileşen analiz -PCA ile etkili karar ağacı makinesi öğrenme algoritması-J48 aracılığı ortaya konulmasıdır. Wistar Albino türü sıçanlarkontrol grubu da dahil olmak üzere 4 farklı grup altında incelenmiştir. Sonuçlar incelendiğinde, PCA’nın karar ağacı makinesi öğrenme algoritması J48 ile birlikte kullanıldığında sınıflndırmadaki başarı oranı %96.25’den %97.50’e arttırdığı gözlenmiştir. Böylece, PCA ile desteklenen J48 algoritmasının, Wistar albino türü sıçanlarının daha karmaşık hale getirilmiş diyabetik metobolik değerlerinden elde edilen veriler üzerinde başarılı bir şekilde kullanılabileceğini ortaya koymuştur.

Classification of Biochemical and Biomechanical Data of Diabetic Rats Treated with Magnetic Field By PCA-Supported J48 Algorithm

The aim of this study was to investigate the J48 mediated decision tree algorithm from the principal component analysis - PCA, which ismore complex, one of the statistical algorithms of diabetic metabolic disorders of Wistar albino rats’ biochemical values and magnetic fieldapplication. Wistar Albino rats were examined under 4 diffrent groups including the control group. When the results were examined, it wasobserved that PCA increased the success rate of classification from 96.25% to 97.50% when used with J48 decision tree algorithm. Thus, thePCA-supported J48 algorithm demonstrated that Wistar albino rats could be successfully used on the data obtained from more complexdiabetic metabolic values.

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