PURPOSE: To develop a machine learning (ML) classifier for predicting post-induction hypotension (PIH) in non-cardiac surgeries. MATERIALS AND METHODS: Preoperative data and early vital signs were obtained from 3669 cases in the VitalDB database, an open-source registry. PIH was defined as sustained mean arterial pressure (MAP) <
65 mm Hg within 20 minutes since induction or from induction to incision. Six different ML algorithms were used to create binary classifiers to predict PIH. The primary outcome was the area under the receiver operating characteristic curve (AUROC) of ML classifiers. RESULTS: A total of 2321 (63.3%) cases exhibited PIH. Among ML classifiers, the random forest regressor and extremely gradient boosting regressor showed the highest AUROC, both recording a value of 0.772. Excluding these models, the light gradient boosting machine regressor showed the second highest AUROC [0.769
95% confidence interval (CI), 0.767-0.771], followed by the gradient boosting regressor (0.768
95% CI, 0.763-0.772), AdaBoost regressor (0.752
95% CI, 0.743-0.761), and automatic relevance determination regression (0.685
95% CI, 0.669-0.701). The top three important features were mean diastolic blood pressure (DBP), minimum MAP, and minimum DBP from anesthetic induction to tracheal intubation, and these features were lower in cases with PIH (all CONCLUSION: ML classifiers exhibited moderate performance in predicting PIH, and have the potential for real-time prediction.