Diyabet Hastalığının Erken Aşamada Tahmin Edilmesi İçin Makine Öğrenme Algoritmalarının Performanslarının Karşılaştırılması

Year 2021, Volume: 9 Issue: 6 - ICAIAME 2021, 123 - 134, 31.12.2021

Kemal Akyol , Abdulkadir Karacı

https://doi.org/10.29130/dubited.1014508

Cited By: 3

Abstract

Şeker hastalığı, kan şekerinde anormalliklere neden olan zararlı hastalıklardan biridir. Bu hastalığın erken teşhisi insan vücudunda oluşabilecek organ bozulmalarını engeller. Yapay zekâ tabanlı çalışmalar medikal alanda etkin bir şekilde gerçekleştirilmektedir. Makine öğrenmesine dayalı bilgisayar destekli uzman sistemler bu hastalığın erken teşhisi için oldukça faydalıdır. Bu çalışmadaki şeker hastalığı problemi, klasik bir denetimli ikili sınıflandırma problemidir. Bu verisetinde 16 öznitelik bulunmakta olup, 200'ü negatif örnek ve 320'si pozitif örnek olmak üzere toplam 520 örnek içermektedir. Önişlemden geçirilen veriseti üzerinde Rastgele Orman, Gradyan Arttırma, K-En Yakın Komşu, Derin Sinir Ağları ve son olarak da Oylama topluluk sınıflandırıcısı kullanılarak inşa edilen modellerin performansları dışarıda tutma ve 5-kat çapraz doğrulama senaryoları çerçevesinde analiz edilmiştir. Her iki senaryoda da, Oylama topluluğu sınıflandırıcısı, deneylerde en iyi performansı sundu. Buna göre, Oylama topluluğu sınıflandırıcısı, tutma tekniğiyle yapılan deneylerde %100'lük bir sınıflandırma doğruluğu ve 5 kat çapraz doğrulamalı deneylerde ortalama %97,31'lik bir sınıflandırma doğruluğu sundu. Sonuç olarak, Oylama topluluğu sınıflandırıcısı kullanılarak diyabeti gerçek zamanlı olarak erken teşhis eden bir uzman sistem tasarlanabilir.

Keywords

Şeker hastalığı, makine öğrenmesi, oylama topluluk sınıflandırıcısı

Thanks

Yazarlar, kamuya açık olan şeker hastalığı veriseti için Islam ve ark. [24]’e teşekkür eder.

Comparison of Performances of Machine Learning Algorithms for Predicting Diabetes Mellitus in Early Stage

Abstract

Diabetes mellitus is one of the harmful diseases that cause abnormalities in blood sugar. Early diagnosis of this disease prevents organ deterioration that may occur in the human body. Artificial intelligence-based studies are carried out effectively in the medical field. Computer-aided expert systems based on machine learning are quite useful for the early detection of this disease. The diabetes mellitus problem in this study is a classical supervised binary classification problem. There are 16 attributes in this dataset also it includes a total of 520 samples, 200 of which are negative samples and 320 of which are positive samples. The performances of the models constructed using Random Forest, Gradient Boosting, K-Nearest Neighbour, Deep Neural Networks, and finally voting ensemble classifier are analyzed within the framework of hold-out and 5-fold cross-validation techniques on the dataset pre-processed. In both scenarios, the Voting ensemble classifier presented the best performance in experiments. Accordingly, the Voting ensemble classifier offered a classification accuracy of 100% in experiments with the hold out technique, and an average of 97.31% in experiments with 5-fold cross-validation. As a result, an expert system that early diagnoses diabetes in real-time can be designed by using the Voting ensemble classifier.

Keywords

Diabetes mellitus, machine learning, voting ensemble classifier.

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