Classification of Breast Cancer on the Strength of Potential Risk Factors with Boosting Models: A Public Health Informatics Application

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AKBULUT A. S., BALIKÇI ÇİÇEK İ., ÇOLAK C.

HASEKI TIP BULTENI-MEDICAL BULLETIN OF HASEKI, cilt.60, sa.3, ss.196-203, 2022 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 60 Sayı: 3
  • Basım Tarihi: 2022
  • Doi Numarası: 10.4274/haseki.galenos.2022.8440
  • Dergi Adı: HASEKI TIP BULTENI-MEDICAL BULLETIN OF HASEKI
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Academic Search Premier, CINAHL, EMBASE, Directory of Open Access Journals, TR DİZİN (ULAKBİM)
  • Sayfa Sayıları: ss.196-203
  • Anahtar Kelimeler: Breast cancer, boosting algorithm, gradient boosting algorithm, XGBoost algorithm, LightGBM algorithm
  • İnönü Üniversitesi Adresli: Evet

Özet

Aim: The diagnosis of breast cancer can be accomplished using an algorithm or an early detection model of breast cancer risk via determining factors. In the present study, gradient boosting machines (GBM), extreme gradient boosting (XGBoost) and light gradient boosting (LightGBM) models were applied and their performances were compared.