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- 저자 : Roya Najafi-Vosough (검색결과 1 건)
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Roya Najafi-Vosough,Javad Faradmal,Seyed Kianoosh Hosseini,Abbas Moghimbeigi,Hossein Mahjub 대한의료정보학회 2021 Healthcare Informatics Research Vol.27 No.4
Objectives: Heart failure (HF) is a common disease with a high hospital readmission rate. This study considered class imbalanceand missing data, which are two common issues in medical data. The current study’s main goal was to compare theperformance of six machine learning (ML) methods for predicting hospital readmission in HF patients. Methods: In thisretrospective cohort study, information of 1,856 HF patients was analyzed. These patients were hospitalized in FarshchianHeart Center in Hamadan Province in Western Iran, from October 2015 to July 2019. The support vector machine (SVM),least-square SVM (LS-SVM), bagging, random forest (RF), AdaBoost, and naïve Bayes (NB) methods were used to predicthospital readmission. These methods’ performance was evaluated using sensitivity, specificity, positive predictive value, negativepredictive value, and accuracy. Two imputation methods were also used to deal with missing data. Results: Of the 1,856HF patients, 29.9% had at least one hospital readmission. Among the ML methods, LS-SVM performed the worst, with accuracyin the range of 0.57–0.60, while RF performed the best, with the highest accuracy (range, 0.90–0.91). Other ML methodsshowed relatively good performance, with accuracy exceeding 0.84 in the test datasets. Furthermore, the performance ofthe SVM and LS-SVM methods in terms of accuracy was higher with the multiple imputation method than with the medianimputation method. Conclusions: This study showed that RF performed better, in terms of accuracy, than other methods forpredicting hospital readmission in HF patients.
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