Diyabet Tahmini İçin Geleneksel Yöntemlerin Analizi ve Değerlendirilmesi

Year 2023, Issue: 52, 220 - 233, 15.12.2023

Canan Batur Şahin , Hayriye Tanyıldız , Özlem Batur Dinler

Abstract

Dünya çapında milyonlarca insanı etkileyen kronik bir hastalık olan diyabet, vücudun kan şekeri düzeylerini etkili bir şekilde yönetememesiyle karakterize edilir. Kontrol edilmezse veya uygun şekilde yönetilmezse, bu durum kalp hastalığı, felç, böbrek yetmezliği ve hatta körlük gibi ciddi sonuçlara yol açabilir. Genetik ve yaşam tarzı faktörlerinin karşılıklı etkileşimi nedeniyle, diyabet insidansı artmakta ve diyabet acil müdahale gerektiren önemli bir küresel sağlık sorunu olarak konumlanmaktadır. Dünya Sağlık Örgütü (WHO), diyabetin küresel prevalansının 1980'den bu yana neredeyse iki katına çıktığını ve yetişkin nüfusta %4,7'den %8,5'e yükseldiğini bildirmektedir. Bu artış, hastalığın erken teşhisine ve etkin yönetimine yönelik stratejilerin aciliyetini ve önemini vurgulamaktadır. Böyle bir halk sağlığı sorunu karşısında sağlık hizmetleri bu salgınla mücadele için teknolojik gelişmelerden yardım istemektedir. Sağlık hizmetlerinde en umut verici teknolojik sınırlar arasında, çok büyük miktarda veriyi analiz edebilen, kalıpları tanımlayabilen ve sonuçları tahmin edebilen yapay zekanın (AI) bir alt kümesi olan Makine Öğrenimi (ML) yer alıyor. Makine öğrenimi, hasta sağlığına ilişkin değerli içgörüler sağlayarak, tedavi kararlarını bildirerek ve hatta bir kişinin gelecekte hastalığa yakalanma riskini tahmin ederek diyabet yönetiminde devrim yaratma potansiyeline sahiptir. Bu teknoloji, doğru kullanılırsa diyabetle mücadelede oyunu değiştirebilir. Bu bağlamda, diyabet riskini tahmin etmek için geleneksel sınıflandırıcı yöntemlerin kullanılması uygulanabilir ve etkili bir yaklaşım gibi görünmektedir. Bu yöntemler gelişmeye devam ettikçe, bu kronik hastalığın erken teşhisi ve etkili tedavisinde önemli bir rol oynamakta ve diyabet risk tahmininin doğruluğunu ve kesinliğini artırma sözü vermektedir.
Bu yazıda, diyabeti tahmin etmek için geleneksel sınıflandırıcı yöntemlerin nasıl kullanıldığını, bu teknolojinin hastalık teşhisindeki etkilerini ve gelişen bu alanın gelecekteki potansiyelini inceleyeceğiz.

Keywords

Diyabet, Yapay zeka, Sınıflandırıcılar, Makine Öğrenmesi, Tahmin.

Analysis and Evaluation of Conventional Methods for Diabetes Prediction

Abstract

Diabetes, a chronic disease that affects millions of people worldwide, is characterized by the body's inability to manage blood sugar levels effectively. If left unchecked or not managed properly, this condition can lead to serious consequences such as heart disease, stroke, kidney failure, and even blindness. Due to the interplay of genetic and lifestyle factors, the incidence of diabetes is increasing, positioning it as a significant global health problem requiring urgent attention.
The World Health Organization (WHO) reports that the global prevalence of diabetes has nearly doubled since 1980, rising from 4.7% to 8.5% in the adult population. This increase highlights the urgency and importance of strategies aimed at early diagnosis and effective management of the disease. In the face of such a public health problem, health services seek help from technological developments to combat this epidemic. Among the most promising technological frontiers in healthcare is Machine Learning (ML), a subset of artificial intelligence (AI) that can analyze vast amounts of data, identify patterns and predict outcomes. Machine learning has the potential to revolutionize diabetes management by providing valuable insights into patient health, informing treatment decisions, and even predicting a person's risk of developing the disease in the future. This technology, if used properly, could change the game in the fight against diabetes. In this context, the use of traditional classifier methods to estimate diabetes risk seems to be a viable and efficient approach. As these methods continue to evolve, they play an important role in the early detection and effective treatment of this chronic disease, promising to increase the accuracy and precision of diabetes risk estimation.
In this article, we will examine how traditional classifier methods are used to predict diabetes, the implications of this technology for disease diagnosis, and the future potential of this evolving field.

Keywords

Diabetes, Artificial Intelligence, Classifiers, Machine Learning, Prediction

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