Mortality Prediction from Hospital-Acquired Infections in Trauma Patients Using an Unbalanced Dataset

Mehrdad Karajizadeh; Mahdi Nasiri; Mahnaz Yadollahi; Amir Hussain Zolfaghari; Ali Pakdam

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자료유형: 학술저널

저자정보: Mehrdad Karajizadeh (Shiraz University of Medical Sciences) Mahdi Nasiri (Shiraz University of Medical Sciences) Mahnaz Yadollahi (Shiraz University of Medical Sciences) Amir Hussain Zolfaghari (Laurentian University) Ali Pakdam (Shiraz University of Medical Sciences)

저널정보: 대한의료정보학회 Healthcare Informatics Research Healthcare Informatics Research 제26권 제4호

발행연도: 2020.1

수록면: 284 - 294 (11page)

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Objectives: Machine learning has been widely used to predict diseases, and it is used to derive impressive knowledge in thehealthcare domain. Our objective was to predict in-hospital mortality from hospital-acquired infections in trauma patientson an unbalanced dataset. Methods: Our study was a cross-sectional analysis on trauma patients with hospital-acquired infectionswho were admitted to Shiraz Trauma Hospital from March 20, 2017, to March 21, 2018. The study data was obtainedfrom the surveillance hospital infection database. The data included sex, age, mechanism of injury, body region injured, severityscore, type of intervention, infection day after admission, and microorganism causes of infections. We developed ourmortality prediction model by random under-sampling, random over-sampling, clustering (k-mean)-C5.0, SMOTE-C5.0,ADASYN-C5.5, SMOTE-SVM, ADASYN-SVM, SMOTE-ANN, and ADASYN-ANN among hospital-acquired infectionsin trauma patients. All mortality predictions were conducted by IBM SPSS Modeler 18. Results: We studied 549 individualswith hospital-acquired infections in a trauma hospital in Shiraz during 2017 and 2018. Prediction accuracy before balancingof the dataset was 86.16%. In contrast, the prediction accuracy for the balanced dataset achieved by random under-sampling,random over-sampling, clustering (k-mean)-C5.0, SMOTE-C5.0, ADASYN-C5.5, and SMOTE-SVM was 70.69%, 94.74%,93.02%, 93.66%, 90.93%, and 100%, respectively. Conclusions: Our findings demonstrate that cleaning an unbalanced datasetincreases the accuracy of the classification model. Also, predicting mortality by a clustered under-sampling approach wasmore precise in comparison to random under-sampling and random over-sampling methods.

#Machine Learning #Mortality #Injuries #Healthcare Associated Infections #Data Mining #Decision Tree #C5.0

참고문헌 (26)

참고문헌 신청

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