Key result
The logistic regression model showed optimal performance in predicting asymptomatic carotid atherosclerosis using electronic health records, achieving an AUCROC of 0.809 and an accuracy of 74.7%.
Why the study?
Early identification of asymptomatic carotid atherosclerosis is challenging and can trigger ischemic stroke, prompting evaluation of machine learning with electronic health records for prediction.
Does logistic regression using electronic health records improve the prediction of asymptomatic carotid atherosclerosis in medical check-up subjects?
Cohort (n=18,441)
No
Does logistic regression using electronic health records improve the prediction of asymptomatic carotid atherosclerosis in medical check-up subjects?
Absolute Event Rate: 0.809% vs 0.628%
Logistic regression using routine electronic health records provides a precise and convenient method for the early identification of asymptomatic carotid atherosclerosis, outperforming other tested machine learning models.
ML models using routine EHR data may support CAS risk stratification; hypothesis-generating and needs prospective validation before any clinical adoption.
BACKGROUND: Screening carotid B-mode ultrasonography is a frequently used method to detect subjects with carotid atherosclerosis (CAS). Due to the asymptomatic progression of most CAS patients, early identification is challenging for clinicians, and it may trigger ischemic stroke. Recently, machine learning has shown a strong ability to classify data and a potential for prediction in the medical field. The combined use of machine learning and the electronic health records of patients could provide clinicians with a more convenient and precise method to identify asymptomatic CAS. METHODS: Retrospective cohort study using routine clinical data of medical check-up subjects from April 19, 2010 to November 15, 2019. Six machine learning models (logistic regression [LR], random forest [RF], decision tree [DT], eXtreme Gradient Boosting [XGB], Gaussian Naïve Bayes [GNB], and K-Nearest Neighbour [KNN]) were used to predict asymptomatic CAS and compared their predictability in terms of the area under the receiver operating characteristic curve (AUCROC), accuracy (ACC), and F1 score (F1). RESULTS: Of the 18,441 subjects, 6553 were diagnosed with asymptomatic CAS. Compared to DT (AUCROC 0.628, ACC 65.4%, and F1 52.5%), the other five models improved prediction: KNN + 7.6% (0.704, 68.8%, and 50.9%, respectively), GNB + 12.5% (0.753, 67.0%, and 46.8%, respectively), XGB + 16.0% (0.788, 73.4%, and 55.7%, respectively), RF + 16.6% (0.794, 74.5%, and 56.8%, respectively) and LR + 18.1% (0.809, 74.7%, and 59.9%, respectively). The highest achieving model, LR predicted 1045/1966 cases (sensitivity 53.2%) and 3088/3566 non-cases (specificity 86.6%). A tenfold cross-validation scheme further verified the predictive ability of the LR. CONCLUSIONS: Among machine learning models, LR showed optimal performance in predicting asymptomatic CAS. Our findings set the stage for an early automatic alarming system, allowing a more precise allocation of CAS prevention measures to individuals probably to benefit most.
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Fan et al. (2021) conducted a cohort in Asymptomatic carotid atherosclerosis (n=18,441). Logistic regression (LR) machine learning model vs. Decision tree (DT) and other machine learning models was evaluated on Area under the receiver operating characteristic curve (AUCROC) for predicting asymptomatic carotid atherosclerosis. The logistic regression model showed optimal performance in predicting asymptomatic carotid atherosclerosis using electronic health records, achieving an AUCROC of 0.809 and an accuracy of 74.7%.
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