A Review on Machine Learning Algorithms in Healthcare

Abstract

In recent years, the issue of improving health processes by using machine learning algorithms by researchers has become a big trend. Machine learning has become a popular and effective method used to improve the quality of healthcare services, prevent disease outbreaks, diagnose diseases early, reduce hospital operating costs, assist the government in healthcare policies, and increase healthcare efficiency. In this review, machine learning studies carried out in the field of health are summarized and classified. In particular, the focus is on studies of non-communicable diseases, which threaten public health and are at the top of the list of causes of death in the world. In addition, the COVID-19 disease, which is on the list of the world's largest deadly diseases and has been declared a public health emergency in recent years, is also included. The purpose of this study is to assist researchers working in the field of health in choosing appropriate algorithms. As a result of the compilation studies, the best performing classification algorithm in healthcare services was Decision Tree(DT), Random Forest (RF), Gaussian Naive Bayes (GNB) with an average accuracy of 100%.

Keywords

• Machine learning algorithms
• healthcare
• communicable diseases
• non-communicable diseases

Sağlık Hizmetlerinde Güncel Makine Öğrenmesi Algoritmaları

Öz

Son yıllarda araştırmacılar tarafından makine öğrenmesi algoritmalarını kullanarak sağlık süreçlerinin iyileştirilmesi konusu büyük bir trend haline gelmiştir. Makine öğrenmesi, sağlık hizmetlerinde kaliteyi yükseltmek, hastalık yayılımlarını önlemek, hastalıkları erken teşhis etmek, hastane operasyon maliyetlerini azaltmak, hükümete sağlık hizmetleri politikalarında yardımcı olmak ve sağlık hizmetinin verimliliğini artırmak için kullanılan popüler ve etkili bir yöntem haline gelmiştir. Bu derleme çalışmasında, sağlık alanında gerçekleştirilen makine öğrenmesi çalışmaları özetlenmiş ve sınıflandırılmıştır. Özellikle halk sağlığını tehdit eden ve dünyada ölüm nedenleri listesinde ilk sıralarda yer alan, bulaşıcı olmayan hastalık çalışmalarına odaklanılmıştır. Ayrıca dünyanın en büyük ölümcül hastalıklar listesinde yer alan ve son yıllarda halk sağlığı için acil durum ilan edilen COVID-19 hastalığına da yer verilmiştir. Bu çalışmanın amacı, sağlık alanında çalışma yapan araştırmacılara uygun algoritmalarını seçmesinde yardımcı olmaktır. Derleme çalışmasının sonucunda sağlık hizmetlerinde en iyi performans gösteren sınıflandırma algoritması ortalama %100 doğruluk başarısıyla Decision Tree (DT), Random Forest (RF), Gaussian Naive Bayes (GNB) olmuştur.

Anahtar Kelimeler

• Makine öğrenmesi algoritmaları
• sağlık hizmetleri
• bulaşıcı hastalıklar
• bulaşıcı olmayan hastalıklar

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