Journal of King Saud University: Science (Apr 2023)

Predicting the outcome of heart failure against chronic-ischemic heart disease in elderly population – Machine learning approach based on logistic regression, case to Villa Scassi hospital Genoa, Italy

  • Done Stojanov,
  • Elena Lazarova,
  • Elena Veljkova,
  • Paolo Rubartelli,
  • Mauro Giacomini

Journal volume & issue
Vol. 35, no. 3
p. 102573

Abstract

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Totally 167 patients were admitted at cardiology ward in Villa Scassi hospital, Genoa, Italy. We worked with two control groups: heart failure 59 patients (mean age: 71.37 ± 13.27 years) and chronic-ischemic heart disease 108 patients (mean age: 68.85 ± 11.3 years). Nine parameters: Hb, Serum Creatinine, LDL, HDL, Triglycerides, ALT, AST, hs-cTnI, CRP were evaluated onset to hospitalization. We aimed to identify significant independent predictors relative to the outcome of heart failure versus chronic-ischemic heart disease and select combination of biochemical parameters in logistic regression-based model that would provide on average excellent discrimination to the outcome of heart failure versus chronic-ischemic heart disease in elderly population. Applying 20-fold repeated stratified cross-validation, 4:1 train/test ratio split, we have found that model: pHF=eα+β1Hb+β2SerumCreatinine+β3AST+β4hs-cTnI+β5CRP1+eα+β1Hb+β2SerumCreatinine+β3AST+β4hs-cTnI+β5CRP, pHF: probability of heart failure, provides best discrimination of the outcome of heart failure against chronic-ischemic heart disease, having learned coefficients: α,β1,β2,β3,β4,β5 upon training set.

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