PREDICTING LUNG CANCER USING EXPLAINABLE ARTIFICIAL INTELLIGENCE AND BORUTA-SHAP METHODS

Erkan Akkur; Ahmet Cankat Öztürk

doi:10.17780/ksujes.1425483

TR EN

AÇIKLANABİLİR YAPAY ZEKA VE BORUTA-SHAP YÖNTEMLERİYLE AKCİĞER KANSERİNİN ÖNGÖRÜLMESİ

Öz

Son yıllarda hastalık tahmini için popüler bir yaklaşım olan makine öğrenmesi algoritmaları, ölümcül etkileri olan akciğer kanserinin tahmininde de kullanılabilir. Bu çalışmada, akciğer kanserini tahmin etmek için makine öğrenmesi algoritmalarına dayalı bir tahmin modeli önerilmiştir. Sınıflandırıcı olarak beş karar ağacı tabanlı algoritma tercih edilmiştir. Deney, risk faktörlerini içeren kamuya açık bir veri seti üzerinde gerçekleştirilmiştir. Veri setindeki en belirgin özellikleri ortaya çıkarmak için Boruta-SHAP yaklaşımı kullanılmıştır. Öznitelik seçim yönteminin kullanılması sınıflandırıcılarının tahmin işleminde göstermiş oldukları performansları artırmıştır. Deneyler tüm özellikler ve indirgenmiş özellikler ayrı ayrı kullanılarak gerçekleştirilmiştir. Tüm sınıflandırıcıların performansları karşılaştırıldığında, 97.22% doğruluk ve 0.972 AUROC ile en iyi tahmin oranını üreten XGBoost algoritması olmuştur. Önerilen model, literatürdeki benzer çalışmalara kıyasla iyi bir sınıflandırma oranına sahiptir. Veri setindeki risk faktörlerinin model çıktısı üzerindeki etkisini araştırmak için SHAP (SHapley Additive exPlanation) yaklaşımını kullandık. Sonuç olarak, alerji bu hastalık için en önemli risk faktörü olarak bulunmuştur.

Anahtar Kelimeler

PREDICTING LUNG CANCER USING EXPLAINABLE ARTIFICIAL INTELLIGENCE AND BORUTA-SHAP METHODS

Abstract

Machine learning algorithms, a popular approach for disease prediction in recent years, can also be used to predict lung cancer, which has fatal effects. A prediction model based on machine learning algorithms is proposed to predict lung cancer. Five decision tree-based algorithms were preferred as classifiers. The experiment was conducted on a publicly available data set that contained risk factors. The Boruta-SHAP approach was employed to reveal the most salient features in the dataset. The use of the feature selection method improved the performance of the classifiers in the prediction process. Experiments were conducted using all features and reduced features separately. When comparing all the classifiers' performances, the XGBoost algorithm produced the best prediction rate with an accuracy of 97.22% and an AUROC of 0.972. The proposed model has a good classification rate compared to similar studies in the literature. We used the SHAP (SHapley Additive exPlanation) approach to investigate the effect of risk factors in the dataset on the model output. As a result, allergy was found to be the most significant risk factor for this disease.

Keywords

Etik Beyan

Bu çalışmada kamuya açık erişimi olan bir veri seti kullanıldı. Bu yüzden, etik kurul iznine ihtiyaç bulunmamaktadır.

Kaynakça

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Ayrıntılar

Birincil Dil

İngilizce

Konular

Makine Öğrenme (Diğer)

Bölüm

Araştırma Makalesi

Yazarlar

Erkan Akkur ^*
0000-0001-5573-5096
Türkiye

Ahmet Cankat Öztürk
0000-0002-7082-6479
Türkiye

Yayımlanma Tarihi

3 Eylül 2024

Gönderilme Tarihi

25 Ocak 2024

Kabul Tarihi

4 Mart 2024

Yayımlandığı Sayı

Yıl 2024 Cilt: 27 Sayı: 3

DOI

https://doi.org/10.17780/ksujes.1425483

IZ

https://izlik.org/JA46BB56TC

Kaynak Göster

RIS / Bibtex

APA

Akkur, E., & Öztürk, A. C. (2024). PREDICTING LUNG CANCER USING EXPLAINABLE ARTIFICIAL INTELLIGENCE AND BORUTA-SHAP METHODS. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 27(3), 792-803. https://doi.org/10.17780/ksujes.1425483