Abstract
Cardiovascular diseases (CVDs) have been reported as one of the leading causes of death worldwide by World Health Organization (WHO). Although CVDs can be treated, it has high risk of recurrence. In this study, we intended to construct the predictive model of cardiovascular disease recurrence by machine learning approach. We used the 18-month prognosis tracing data to construct and evaluate the recurrence predictive model. We collected 36 physiological factors associated to the cardiovascular disease recurrence identified from literature from 1274 cardiovascular disease inpatients as they discharged from the hospital and their follow-up prognoses after six months. To address the imbalance data problems that are prevalent in medical dataset, we revised the ensemble learning method by performing multiple undersampling to construct a committee of SVM classifiers. The evaluation results show that our proposed approach outperforms all benchmarks in term of F1 measure and Area under ROC curve (AUROC). Our study has demonstrated an approach to address to construct an effective prediction model for cardiovascular diseases recurrence. It might also support physicians in assessing patients who are at the high risk of recurrence.
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Lee, YH., Lin, TK., Huang, YY., Chu, TH. (2022). An Ensemble Learning Method for Constructing Prediction Model of Cardiovascular Diseases Recurrence. In: Fui-Hoon Nah, F., Siau, K. (eds) HCI in Business, Government and Organizations. HCII 2022. Lecture Notes in Computer Science, vol 13327. Springer, Cham. https://doi.org/10.1007/978-3-031-05544-7_16
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