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- W3048421757 abstract "Predictive analytics assist and improve healthcare management of patients as follow-ups to percutaneous coronary intervention (PCI) procedure. This research aimed to construct, validate, and measure performance of three predictive models in predicting major adverse cardiac events (MACE) within one year after PCI. This study utilized data of 10511 patients who underwent PCI at the National Heart Institute (IJN) between 2008 and 2012. The data mainly consisted of information on patientÂs demographic, health status before and during the PCI procedure, and MACE outcome within one year after the procedure. The data was split into development and validation datasets. After variable selection process, predictive models based on logistic regression, artificial neural network (ANN), and decision tree were developed using the development data set. Each model then validated using the validation dataset. Performance of the models were measured using Matthews correlation coefficient (MCC). MACE occurred amongst 12.9% patients during 1-year follow-up. Fifteen predictors were identified to significantly associated with 1-year MACE. ANN model gave the best performance, followed by logistic regression and decision tree.ANN is a reliable model to predict MACE case after PCI procedure. The findings enlighten clinicians and patients to plan better management after PCI." @default.
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- W3048421757 date "2020-01-01" @default.
- W3048421757 modified "2023-09-27" @default.
- W3048421757 title "PREDICTING MAJOR ADVERSE CARDIAC EVENTS (MACE) ONE YEAR AFTER PERCUTANEOUS CORONARY INTERVENTION (PCI) PROCEDURE: DEVELOPMENT AND VALIDATION OF MODELS." @default.
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