A New Clinical Decision Support System for Early Diagnosis of Chronic Kidney Disease

Year 2020, Issue: 20, 448 - 455, 31.12.2020

Can Eyüpoğlu

https://doi.org/10.31590/ejosat.743652

Cited By: 1

Abstract

Chronic kidney disease is a worldwide health problem. It is possible to slow or stop the progression of this disease thanks to early diagnosis and treatment. Clinical decision support systems are health information technology systems designed to assist medical doctors in clinical decision making tasks. In this study, a new clinical decision support system is proposed for the early diagnosis of chronic kidney disease. Principal component analysis (PCA) and random forests (RF) techniques are used in the feature extraction and classification phases of the proposed system, respectively. The performance of the proposed system has been compared with classical machine learning algorithms and previous studies in the literature using six different performance metrics. The test results show that the proposed system is successful and can assist doctors in making decisions for early diagnosis of chronic kidney disease.

Keywords

Chronic Kidney Disease, Early Diagnosis, Clinical Decision Support System, Principal Component Analysis, Random Forests

Kronik Böbrek Hastalığının Erken Tanısı için Yeni Bir Klinik Karar Destek Sistemi

Abstract

Kronik böbrek hastalığı dünya çapında bir sağlık sorunudur. Erken tanı ve tedavi sayesinde bu hastalığın ilerlemesini yavaşlatmak veya durdurmak mümkün olmaktadır. Klinik karar destek sistemleri, tıp doktorlarına klinik karar verme görevlerinde yardımcı olmak amacıyla tasarlanan sağlık bilgi teknolojisi sistemleridir. Bu çalışmada kronik böbrek hastalığının erken tanısı için yeni bir klinik karar destek sistemi önerilmiştir. Önerilen sistemin özellik çıkarma ve sınıflandırma aşamalarında sırasıyla temel bileşen analizi (principal component analysis-PCA) ve rastgele ormanlar (random forests-RF) teknikleri kullanılmıştır. Önerilen sistemin performansı, altı farklı performans metriği ile klasik makine öğrenmesi algoritmaları ve literatürde daha önce yapılan çalışmalar ile kıyaslanmıştır. Test sonuçları, önerilen sistemin başarılı olduğunu ve kronik böbrek hastalığının erken tanısı için karar vermede doktorlara destek olabileceğini göstermektedir.

Keywords

Kronik Böbrek Hastalığı, Erken Tanı, Klinik Karar Destek Sistemi, Temel Bileşen Analizi, Rastgele Ormanlar

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