Empirical thresholding logistic regression model based on unbalanced cardiac patient data

Iris Reychav, Lin Zhu, Roger McHaney, Yaron Arbel

Research output: Contribution to journalConference articlepeer-review


Cardiac disease causes widespread morbidity and mortality. Past research in this area focused on risk factors and treatment. Little exists on patient survival classification in emergency room situations with unbalanced data. The current study expanded knowledge in this area based on over 2,000 cardiac patient records. This unbalanced dataset was used to develop an empirical, thresholding logistic regression model which predicted patients' survival. The model was refined using stepwise and cost-efficient methods. The exploration revealed important factors that influenced patient survival and suggested a thresholding logistic regression model can provide a flexible and pragmatic way to handle unbalanced cardiac patient data. The model identified key factors to help doctors concentrate on important indicators related to patient survival. This study offers novel technical and practical insights for instant survival analysis of cardiac patients, using an unbalanced dataset.


  • Classification
  • Empirical Thresholding Logistic Regression
  • Survival Prediction
  • Unbalanced Cardiac Patients Data

All Science Journal Classification (ASJC) codes

  • General Computer Science


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