A Comparison among Different Machine Learning Pretest Approaches to Predict Stress-Induced Ischemia at PET/CT Myocardial Perfusion Imaging.

Journal: Computational and mathematical methods in medicine
PMID: 34873413

Abstract

Traditional approach for predicting coronary artery disease (CAD) is based on demographic data, symptoms such as chest pain and dyspnea, and comorbidity related to cardiovascular diseases. Usually, these variables are analyzed by logistic regression to quantifying their relationship with the outcome; nevertheless, their predictive value is limited. In the present study, we aimed to investigate the value of different machine learning (ML) techniques for the evaluation of suspected CAD; having as gold standard, the presence of stress-induced ischemia by Rb positron emission tomography/computed tomography (PET/CT) myocardial perfusion imaging (MPI) ML was chosen on their clinical use and on the fact that they are representative of different classes of algorithms, such as deterministic (Support vector machine and Naïve Bayes), adaptive (ADA and AdaBoost), and decision tree (Random Forest, rpart, and XGBoost). The study population included 2503 consecutive patients, who underwent MPI for suspected CAD. To testing ML performances, data were split randomly into two parts: training/test (80%) and validation (20%). For training/test, we applied a 5-fold cross-validation, repeated 2 times. With this subset, we performed the tuning of free parameters for each algorithm. For all metrics, the best performance in training/test was observed for AdaBoost. The Naïve Bayes ML resulted to be more efficient in validation approach. The logistic and rpart algorithms showed similar metric values for the training/test and validation approaches. These results are encouraging and indicate that the ML algorithms can improve the evaluation of pretest probability of stress-induced myocardial ischemia.

Authors

  • Rosario Megna
    Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy.
  • Mario Petretta
    Department of Translational Medical Sciences, University of Naples "Federico II,", Naples, Italy.
  • Roberta Assante
    Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy.
  • Emilia Zampella
    Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy.
  • Carmela Nappi
    Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy.
  • Valeria Gaudieri
    Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy.
  • Teresa Mannarino
    Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy.
  • Adriana D'Antonio
    Department of Advanced Biomedical Sciences, University Federico II, Naples, Italy.
  • Roberta Green
    Department of Advanced Biomedical Sciences, University of Naples "Federico II", Via Pansini 5, 80131, Naples, Italy.
  • Valeria Cantoni
    Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Naples, Italy.
  • Parthiban Arumugam
    Department of Nuclear Medicine, Central Manchester Foundation Trust, Manchester, UK.
  • Wanda Acampa
    Institute of Biostructure and Bioimaging, National Council of Research, Naples, Italy.
  • Alberto Cuocolo
    Department of Advanced Biomedical Sciences, University Hospital of Naples 'Federico II', Naples, Italy.

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