Handling imbalanced class problem for the prediction of atrial fibrillation in obese patient

ÇOLAK C., KARAASLAN E., ÇOLAK C., ARSLAN A. K., ERDİL N.

BIOMEDICAL RESEARCH-INDIA, cilt.28, sa.7, ss.3293-3299, 2017 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 28 Sayı: 7
  • Basım Tarihi: 2017
  • Dergi Adı: BIOMEDICAL RESEARCH-INDIA
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Chemical Abstracts Core
  • Sayfa Sayıları: ss.3293-3299
  • Anahtar Kelimeler: Imbalanced dataset classification, Atrial fibrillation GLMBoost, LogitBoost, Synthetic minority oversampling technique, KNOWLEDGE DISCOVERY, CORONARY SURGERY, CARDIAC-SURGERY, RISK-FACTORS, SELECTION
  • İnönü Üniversitesi Adresli: Evet

Özet

Objective: Atrial Fibrillation (AF) is one of the important public health problems with elevated comorbidity, advanced mortality risk, and increasing healthcare costs. In this study, the objective is to explore and resolve the imbalanced class problem for the prediction of AF in obese individuals and to compare the predictive results of balanced and imbalanced datasets by several data mining approaches.