A stacking-based model for predicting 30-day all-cause hospital readmissions of patients with acute myocardial infarctio
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RESEARCH ARTICLE
A stacking-based model for predicting 30day all-cause hospital readmissions of patients with acute myocardial infarction Zhen Zhang1,2, Hang Qiu1,2* , Weihao Li3,4 and Yucheng Chen3,4*
Abstract Background: Acute myocardial infarction (AMI) is a serious cardiovascular disease, followed by a high readmission rate within 30-days of discharge. Accurate prediction of AMI readmission is a crucial way to identify the high-risk group and optimize the distribution of medical resources. Methods: In this study, we propose a stacking-based model to predict the risk of 30-day unplanned all-cause hospital readmissions for AMI patients based on clinical data. Firstly,we conducted an under-sampling method of neighborhood cleaning rule (NCR) to alleviate the class imbalance and then utilized a feature selection method of SelectFromModel (SFM) to select effective features. Secondly, we adopted a self-adaptive approach to select base classifiers from eight candidate models according to their performances in datasets. Finally, we constructed a three-layer stacking model in which layer 1 and layer 2 were base-layer and level 3 was meta-layer. The predictions of the base-layer were used to train the meta-layer in order to make the final forecast. Results: The results show that the proposed model exhibits the highest AUC (0.720), which is higher than that of decision tree (0.681), support vector machine (0.707), random forest (0.701), extra trees (0.709), adaBoost (0.702), bootstrap aggregating (0.704), gradient boosting decision tree (0.710) and extreme gradient enhancement (0.713). Conclusion: It is evident that our model could effectively predict the risk of 30-day all cause hospital readmissions for AMI patients and provide decision support for the administration. Keywords: Acute myocardial infarction, Hospital readmission, Clinical data, Machine learning, Self-adaptive, Stackingbased model learning Background Acute myocardial infarction (AMI) is a critical global health issue which causes more than 7 million deaths worldwide per year [1]. According to the evaluation of Healthcare Cost and Utilization Project (HCUP), approximately one in six patients with AMI would have readmission within 30 days of discharge [2]. The high *Correspondence: [email protected]; [email protected] 2 Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China 3 Cardiology Division, West China Hospital, Sichuan University, No.17 People’s South Road,Chengdu, 610041 Chengdu, Sichuan, PR China Full list of author information is available at the end of the article
readmission rate poses a tremendous burden on both the patient and the healthcare system. There is an increasing interest in the rate of readmission as an indicator of the quality of hospital care and prognosis of patients [3]. Effective prediction of 30-days all-cause readmission for AMI patients is capable of identifying patients with high risk for readmission, maximizing the potential for successful intervention,
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