KANSERLİ HASTALARDA MAKİNE ÖĞRENİMİ İLE SAĞKALIM ORANININ TAHMİN EDİLMESİ

Year 2024, Volume: 14 Issue: 28, 842 - 855, 30.11.2024

Cuma Çakmak , Fadime Çınar , Mehmet Aziz Çakmak

https://doi.org/10.53092/duiibfd.1494646

Abstract

Kanser önemli bir halk sağlığı sorunu olmakla birlikte ABD’de hastalık yükü açısından ikinci sırada yer almakta dünyada ise küresel hastalık yükü sıralamasında ilk sıralarda yer alabilmektedir. Önemli oranda mortalite ve morbiditeye neden olan kanser hastalığı birçok faktörden etkilenmektedir. Gerek hastalığa neden olan faktörlerin incelenmesi gerek hastalığın yönetilmesi konusunda araştırmacılar giderek artan oranda bu alanla ilgilenmekte yeni tedavi yöntemleri, yeni teknikler ve teknolojiler ile bu hastalık üzerinde araştırmalar yapmaktadırlar. Bu çalışmada ABD toplumun %8,3’ünü temsil eden ve açık erişimli olarak ulaşılabilen kanser verileri analiz edilerek sağkalım oranlarını tespit etmek amaçlanmıştır. Araştırmada veri madenciliği yöntemlerinden biri olan Konstanz Information Miner (KNIME) programı kullanılmıştır. Elde edilen veriler ile kanser hastalarının sağkalımları sınıflandırılmaya çalışılmıştır. Araştırma kapsamında veri madenciliği araçları olan karar ağaçları, random forrest, Destek Vektör Makineleri (Support Vector Machine-SVM) algoritmaları ile çeşitli güven düzeyleri elde edilmiştir. En yüksek güven düzeyi %75,3 ile random forrest algoritması ile elde edilmiştir. Sonuç olarak modelin anlamlı ve kullanılabilir olduğu ve elde edilen veriler ile sağkalım sınıflandırılmasının yapılabildiği görülmüştür. Sağkalım sınıflandırması kaynak tahsisinde ve etkili bakım konusunda sağlık hizmet sunucuları için önemli bir unsur olabilir.

Keywords

Knime, Veri Madenciliği, Kanser, Ülke Veritabanı, ABD.

PREDICTING SURVIVAL LIMITATION BY MACHINE LEARNING IN PATIENT WITH CANCER

Abstract

Cancer is an important public health problem, ranking second in terms of burden of disease in the United States and ranking first in the global burden of disease in the world. Cancer, which causes significant mortality and morbidity, is affected by many factors. Researchers are increasingly interested in this field, both in examining the factors that cause the disease and in managing the disease and are conducting research on this disease with new treatment methods, new techniques and technologies. In this study, its aimed to determine survival rates by analysing open access cancer data representing 8.3% of the US population. With the data obtained, it was tried to classify the survival of cancer patients. Within the scope of the research, various confidence levels were obtained with decision trees, random forrest, SVM algorithms, which are data mining tools. The highest confidence level was obtained with the random forrest algorithm with 75.3%. As a result, it was seen that the model was meaningful and usable, and that survival classification could be made with the data obtained. Survival classification can be an important element for health service providers in resource allocation and effective care.

Keywords

Knime, Data Mining, Cancer, Country Database, USA.

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