Application of support vector machine classifiers to preoperative risk stratification with myocardial perfusion scintigraphy

Tomotaka Kasamatsu, Jun Hashimoto, Hitoshi Iyatomi, Tadaki Nakahara, Jingming Bai, Naoto Kitamura, Koichi Ogawa, Atsushi Kubo

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Background: Myocardial perfusion single-photon emission computed tomography (SPECT) has been used for risk stratification before non-cardiac surgery. However, few authors have used mathematical models for evaluating the likelihood of perioperative cardiac events. Methods and Results: This retrospective cohort study collected data of 1,351 patients referred for SPECT before non-cardiac surgery. We generated binary classifiers using support vector machine (SVM) and conventional linear models for predicting perioperative cardiac events. We used clinical and surgical risk, and SPECT findings as input data, and the occurrence of all and hard cardiac events as output data. The area under the receiver-operating characteristic curve (AUC) was calculated for assessing the prediction accuracy. The AUC values were 0.884 and 0.748 in the SVM and linear models, respectively in predicting all cardiac events with clinical and surgical risk, and SPECT variables. The values were 0.861 (SVM) and 0.677 (linear) when not using SPECT data as input. In hard events, the AUC values were 0.892 (SVM) and 0.864 (linear) with SPECT, and 0.867 (SVM) and 0.768 (linear) without SPECT. Conclusion: The SVM was superior to the linear model in risk stratification. We also found an incremental prognostic value of SPECT results over information about clinical and surgical risk.

Original languageEnglish
Pages (from-to)1829-1835
Number of pages7
JournalCirculation Journal
Issue number11
Publication statusPublished - 2008


  • Gated single-photon emission computed tomography
  • Myocardial perfusion
  • Perioperative cardiac event
  • Perioperative risk stratification
  • Support vector machine

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine


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