An early aortic dissection screening model and applied research based on ensemble learning

Lijue Liu; Shiyang Tan; Yi Li; Jingmin Luo; Wei Zhang; Shihao Li

doi:10.21037/atm-20-1475

An early aortic dissection screening model and applied research based on ensemble learning

Ann Transl Med. 2020 Dec;8(23):1578. doi: 10.21037/atm-20-1475.

Authors

Lijue Liu^{1

2}, Shiyang Tan¹, Yi Li^{1

2}, Jingmin Luo³, Wei Zhang³, Shihao Li¹

Affiliations

¹ School Of Information Science And Engineering, Central South University, Changsha, China.
² Hunan ZIXING Artificial Intelligence Research Institute, Changsha, China.
³ Xiangya Hospital, Central South University, Changsha, China.

Abstract

Background: As a particularly dangerous and rare cardiovascular disease, aortic dissection (AD) is characterized by complex and diverse symptoms and signs. In the early stage, the rate of misdiagnosis and missed diagnosis is relatively high. This study aimed to use machine learning technology to establish a fast and accurate screening model that requires only patients' routine examination data as input to obtain predictive results.

Methods: A retrospective analysis of the examination data and diagnosis results of 53,213 patients with cardiovascular disease was conducted. Among these samples, 802 samples had AD. Forty-two features were extracted from the patients' routine examination data to establish a prediction model. There were five ensemble learning models applied to explore the possibility of using machine learning methods to build screening models for AD, including AdaBoost, XGBoost, SmoteBagging, EasyEnsemble and XGBF. Among these, XGBF is an ensemble learning model that we propose to deal with the imbalance of the positive and negative samples. The seven-fold cross validation method was used to analyze and verify the performance of each model. Due to the imbalance of the samples, the evaluation indicators were sensitivity and specificity.

Results: Comparative experiments showed that the sensitivity of XGBF was 80.5%, which was better than the 16.1% of AdaBoost, 15.7% of XGBoost, 78.0% of SmoteBagging and 77.8% of EasyEnsemble. Additionally, XGBF had relatively high specificity, and the training time consumption was short. Based on these three indicators, XGBF performed best, and met the application requirements, which means through careful design, we can use machine learning technology to achieve early AD screening.

Conclusions: Through reasonable design, the ensemble learning method can be used to build an effective screening model. The XGBF has high practical application value for screening for AD.

Keywords: Aortic dissection (AD); early screening; ensemble learning; machine learning.