Diyabet hastalığının farklı sınıflandırıcılar kullanılarak teşhisi

Yıl 2023, Cilt: 38 Sayı: 2, 989 - 1002, 07.10.2022

Onur Sevli

https://doi.org/10.17341/gazimmfd.880750

Cited By: 3

Öz

Diyabet dünya genelinde görülme oranı giderek artan, yaygın sağlık sorunlarından biridir. Kronik bir hastalık olan diyabet kontrol altına alınmadığı takdirde göz, kalp, böbrek gibi birçok organda tahribata ve ölümlere neden olabilmektedir. Diyabetin erken teşhisi oluşabilecek komplikasyonları önleme ve yaşam kalitesini arttırma açısından önemlidir. Medikal alanda yaygın kullanılan makine öğrenmesi teknikleri farklı hastalıkların teşhisinde uzmanlar için zeki birer karar destek sistemi rolü üstlenmektedir. Bu çalışma, diyabetin erken teşhisine yönelik olarak 6 farklı makine öğrenmesi tekniği ile PIMA diyabet veri seti üzerinde gerçekleştirilen sınıflama çalışmalarını içermektedir. Sınıflama çalışmalarındaki temel amaç tahmin doğruluğunu arttırmaktır. Bu çalışmada sınıflandırıcıların başarıları arttırmak için veri seti üzerinde 14 farklı yeniden örnekleme yöntemi kullanılmıştır. Her bir makine öğrenmesi modeli için örnekleme olmaksızın ve yeniden örnekleme yapılarak, 90 sınıflama işlemi gerçekleştirilmiştir. Her bir sınıflandırma işleminin başarısı 5 farklı performans metriği ile raporlanmıştır. En başarılı sonuç %96,296 doğrulukla, InstanceHardnessThreshold az örnekleme tekniği ile birlikte Rastgele Orman modelinin kullanıldığı sınıflandırma işleminde elde edilmiştir. Yeniden örnekleme tekniklerinin genel olarak sınıflandırıcıların başarılarını arttırdığı ve kolektif öğrenme yöntemleri ile birlikte kullanıldığında daha başarılı sonuç verdiği görülmüştür. Literatürde aynı veri seti üzerinde, çeşitli makine öğrenmesi yöntemleri kullanılarak yapılan en son çalışmalar ile kıyaslandığında, bu çalışmada elde edilen başarının diğerlerinden daha yüksek ortaya konmuştur.

Anahtar Kelimeler

Diyabet teşhisi, makine öğrenmesi, yeniden örnekleme

Kaynakça

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