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, vol.28, no.7, pp.3293-3299, 2017 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 28 Issue: 7
  • Publication Date: 2017
  • Journal Name: BIOMEDICAL RESEARCH-INDIA
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Chemical Abstracts Core
  • Page Numbers: pp.3293-3299
  • Keywords: Imbalanced dataset classification, Atrial fibrillation GLMBoost, LogitBoost, Synthetic minority oversampling technique, KNOWLEDGE DISCOVERY, CORONARY SURGERY, CARDIAC-SURGERY, RISK-FACTORS, SELECTION
  • Inonu University Affiliated: Yes

Abstract

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.