Turkiye Klinikleri Journal of Biostatistics

.: ORIGINAL RESEARCH
A Novel Variable Selection Procedure for Binary Logistic Regression Using Akaike Information Criteria Testing:An Example in Breast Cancer Prediction: Methodological Study (Validity Study)
Akaike Bilgi Kriterleri Testi Kullanılarak İkili Lojistik Regresyon için Yeni Bir Değişken Seçim Prosedürü: Meme Kanseri Tahmininde Bir Örnek: Metodolojik Çalışma (Geçerlilik Çalışması)
Oyebayo Ridwan OLANIRANa, Saidat Fehintola OLANIRANb
aDepartment of Statistics, Faculty of Physical Sciences, University of Ilorin, Ilorin, PMB 1515, Kwara State, Nigeria
bDepartment of Statistics and Mathematical Sciences, Faculty of Pure and Applied Science, Kwara State University, PMB 1530 Malete, Kwara State, Nigeria
Turkiye Klinikleri J Biostat. 2023;15(2):91-101
doi: 10.5336/biostatic.2023-97597
Article Language: EN
Full Text
ABSTRACT
 Objective: Breast cancer is a leading cause of cancer-related death among women worldwide, with approximately 2.3 million new cases and 685,000 deaths reported in 2020 alone. One critical step in developing effective classification and prediction models is variable selection, which involves identifying a subset of relevant variables from a larger set of potential predictors. Accurate variable selection is crucial for building interpretable and robust models that are not overfit to noise, leading to improved model performance and generalization ability. In this paper, we proposed an alternative objective approach for comparing two Akaike Information Criterions (AIC) that originated from two competing models, such that the magnitude of the difference is subjected to the statistical test of significance. Material and Methods: We developed a new backward elimination variable selection procedure similar in spirit to the existing ''stepAIC'' within the environment of R statistical software. We used both simulated and Wisconsin breast cancer diagnostic datasets to compare the proposed method's variable selection and predictive performances with ''stepAIC'' and LASSO. Results: The simulation showed that the proposed AIC procedure achieved higher variable selection sensitivity, specificity and accuracy when compared to stepAIC and LASSO. Also, the proposed AIC method's prediction results are relatively comparable with stepAIC and LASSO at various simulated data dimensions. Similar supremacy results were observed with the breast cancer dataset used. Conclusion: The AIC-based variable selection approach proposed is a promising method that integrates AIC with statistical testing for improved variable selection in breast cancer classification and prediction.

Keywords: Breast cancer; Akaike Information Criteria; variable selection; backward selection; LASSO
ÖZET
 Amaç: Göğüs kanseri, yalnızca 2020 yılında bildirilmiş yaklaşık 2,3 milyon yeni vaka ve 685.000 ölüm ile dünya çapında kadınlar arasında kanser ilişkili ölümlerin başında gelen sebeplerinden biridir. Etkili sınıflandırma ve tahmin modelleri geliştirmede kritik bir adım, daha geniş bir potansiyel öngörücü setinden, ilgili değişken alt seti tanımlamayı içeren değişken seçimdir. Doğru değişken seçimi, gürültüye fazla uyum sağlamayan, yorumlanabilir ve sağlam modeller oluşturmada çok önemlidir. Bu durum gelişmiş model performansı ve generalizasyon becerisi sağlar. Bu makalede, 2 rakip modelden oluşan 2 Akaike Bilgi Kriterleri''ni [Akaike Information Criterions (AIC)] karşılaştırdığımız alternatif objektif bir yaklaşım sunduk, öyle ki farkın büyüklüğü istatistiksel anlamlılık testine tabi tutulmuştur. Gereç ve Yöntemler: R istatistik yazılımı ortamında bulunan ''stepAIC''ye benzer yeni bir geriye dönük eleme değişken seçme prosedürü geliştirdik. Sunulan metodun değişken seçimi ile ''stepAIC'' ve LASSO ile tahmini performanslarını karşılaştımak için simüle edilmiş, Wisconsin meme kanseri tanı veri setlerini kullandık. Bulgular: Simülasyon, sunulan AIC prosedürünün stepAIC ve LASSO'ya kıyasla yüksek değişken seçim hassasiyeti, spesifitesi ve doğruluğu kazandığını göstermiştir. Ayrıca, sunulan AIC yönteminin tahmin sonuçları, simüle edilen çeşitli veri boyutlarında stepAIC ve LASSO ile görece karşılaştırılabilirdir. Kullanılan meme kanseri veri setinde de benzer üstünlük sonuçları gözlemlenmiştir. Sonuç: AIC temelli değişken seçim yaklaşımı, meme kanseri sınıflandırması ve tahmininde AIC'yi gelişmiş değişken seçimi için istatistiksel testlere entegre eden, umut verici bir metottur.

Anahtar Kelimeler: Meme kanseri; Akaike Bilgi Kriterleri; değişken seçimi; geriye dönük seçim; LASSO
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