Akciğer Kanseri Tanısı ve Prognozunu İyileştirmek için Rastgele Orman Modelinin Kullanımı: Pilot Çalışma

Yıl 2024, Cilt: 2 Sayı: 2, 197 - 204, 30.12.2024

Esin Bilgin , Michelle M. Zhu

Öz

IoT ağının sağlık sistemlerine uygulanmasına yönelik araştırma alanları ve çalışmaları giderek artmaktadır. Çalışmamız öncelikle Rastgele Orman (RF) modelinin akciğer kanseri veri kümelerini analiz etmede ve eğilimleri ve kalıpları tanımada etkinliğini vurgulamaktadır. Yapılan çalışma akciğer kanserinin farklı evrelerini tespit edebilmekte ve hastalığın ilerleyişi hakkında tahmin yürütebilmektedir. Çalışmamız, makine öğrenimi yaklaşımını kullanarak otomatik ve kişiselleştirilmiş teşhis sunabilir. Sağlık sisteminde önemli bir sorun olan akciğer kanserinin tespitinde rastgele orman algoritmasının etkinliği kullanılmış ve başarılı sonuçlar elde edilmiştir. Yaklaşımımız, bir karar destek yöntemi olarak sağlık profesyonellerine faydalı tavsiyeler sağlayabilir. Modelin etkinliğini ölçmek için doğruluk, kesinlik, duyarlılık ve F1 puanları gibi performans ölçümleri toplandı. Bu araştırma, akciğer kanseri hastalarının düşük hata ve yüksek hızda teşhis ve tedavisinde büyük potansiyele sahip olduğunu ortaya koymaktadır.
Anahtar Kelimeler: Akciğer kanseri tahmini, akciğer kanseri sınıflandırması, makine öğrenmesi, rastgele orman.

Anahtar Kelimeler

akciger kanseri, makine ogrenimi, ratgele orman

Using the Random Forest Model to Improve Lung Cancer Diagnosis and Prognosis: A Pilot

Öz

There have been increasing research interests and efforts on the application of IoT network to healthcare systems. Our study primarily highlights the effectiveness of the Random Forest (RF) model in analyzing Lung cancer datasets and recognizing trends and patterns. Our study can detect different stages of lung cancer and predict the disease progression. Our work can offer automated and personalized diagnosis using a machine learning approach. The effectiveness of the random forest algorithm was used in the detection of lung cancer, which is an important problem area in the healthcare system, and successful results were obtained. Our approach can provide useful advice to healthcare professionals as a decision support method. Performance metrics such as accuracy, precision, recall and F1 scores were collected to measure the effectiveness of the model. This research has great potential in the diagnosis and treatment of lung cancer patients with low errors and fast speed.
Keywords: Lung cancer prediction, lung cancer classification, machine learning, random forest.

Anahtar Kelimeler

lung cancer, machine learning, random forest

Kaynakça

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