Medical Informatics IV

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DOI :10.26650/B/T3.2024.40.010   IUP :10.26650/B/T3.2024.40.010    Full Text (PDF)

Comparative Analysis of Machine Learning Methods in Disease Detection Application to Diabetes Mellitus and Heart Diseases

Cihan Çiftçi

The rapid advancement of technology and developments in computer science have brought about significant changes in the field of health, as in many other fields. Artificial intelligence and machine learning have become an important trend in early recognition of the effects that cause diseases, investigating the symptoms of the disease, making the correct diagnosis, and classifying the disease, improving health processes, early diagnosis of diseases, preventing the spread of diseases, reducing health costs, and increasing the effectiveness and quality of health services. has arrived. In this study, machine learning methods were used to detect diabetes and heart diseases, which are among the world’s deadliest diseases and threaten public health. The performances of machine learning algorithms in correctly diagnosing diseases were compared. In this study, by applying the Smote technique, better accuracy results were obtained with Gradient Boosting (GB), Random Forest Classifier (RF), Extreme Gradient Boosting (XGB) methods.


Keywords: Data mining, machine learning algorithms, machine learning in health, disease classification, early diagnosis
DOI :10.26650/B/T3.2024.40.010   IUP :10.26650/B/T3.2024.40.010    Full Text (PDF)

Maki̇ne Öğrenmesi̇ Yöntemleri̇ni̇n Hastalık Tespi̇ti̇nde Karşılaştırmalı Anali̇zi̇: Di̇abetes Melli̇tus ve Kalp Hastalıklarina Uyarlanması

Cihan Çiftçi

Teknolojinin hızla ilerlemesi ve bilgisayar bilimlerindeki gelişmeler birçok alanda olduğu gibi sağlık alanında da önemli değişiklikleri beraberinde getirmiştir. Yapay zeka ve makine öğrenmesi, hastalıklara neden olan etkilerin erken tanınması, hastalığın semptomlarının araştırılması, doğru tanının konulması ve hastalığın sınıflandırılması, sağlık süreçlerinin iyileştirilmesi, hastalıkların erken teşhisi, hastalıkların yayılmasının önlenmesinde önemli bir trend haline gelmiştir. Ayrıca sağlık maliyetlerinin azaltılması, sağlık hizmetlerinin etkinliğinin ve kalitesinin artırılmasında önemli faydalar sağlamaktadır. Bu çalışmada, dünyanın en ölümcül hastalıkları arasında yer alan ve halk sağlığını tehdit eden diyabet ve kalp hastalıklarının tespiti için makine öğrenmesi yöntemlerinden yararlanılmıştır. Makine öğrenmesi algoritmalarının hastalıkların doğru teşhis edilmesindeki performansları karşılaştırıldı. Bu çalışmada Smote tekniği uygulanarak Gradient Boosting (GB), Random Forest Classifier (RF), Extreme Gradient Boosting (XGB) yöntemleriyle daha iyi doğrulukta sonuçlar elde edilmiştir.


Keywords: Veri madenciliği, makine öğrenmesi algoritmaları, sağlıkta makine öğrenmesi, hastalık sınıflandırması, erken teşhis

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