Non-fasting triglyceride levels in the Korean population with and without ischemic heart disease and cerebrovascular disease

A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome

OBJECTIVE

Some researchers have studied about early prediction and diagnosis of major adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms.

METHODS

We used the Korea Acute Myocardial Infarction Registry dataset and selected 11,189 subjects among 13,104 with the 2-year follow-up. It was subdivided into two groups (ST-segment elevation myocardial infarction (STEMI), non ST-segment elevation myocardial infarction NSTEMI), and then subdivided into training (70%) and test dataset (30%). Third, we selected the ranges of hyper-parameters to find the best prediction model from random forest (RF), extra tree (ET), gradient boosting machine (GBM), and SVE. We generated each ML-based model with the best hyper-parameters, evaluated by 5-fold stratified cross-validation, and then verified by test dataset. Lastly, we compared the performance in the area under the ROC curve (AUC), accuracy, precision, recall, and F-score.

RESULTS

The accuracies for RF, ET, GBM, and SVE were (88.85%, 88.94%, 87.84%, 90.93%) for complete dataset, (84.81%, 85.00%, 83.70%, 89.07%) STEMI, (88.81%, 88.05%, 91.23%, 91.38%) NSTEMI. The AUC values in RF were (98.96%, 98.15%, 98.81%), ET (99.54%, 99.02%, 99.00%), GBM (98.92%, 99.33%, 99.41%), and SVE (99.61%, 99.49%, 99.42%) for complete dataset, STEMI, and NSTEMI, respectively. Consequently, the accuracy and AUC in SVE outperformed other ML models.

CONCLUSIONS

The performance of our SVE was significantly higher than other machine learning models (RF, ET, GBM) and its major prognostic factors were different. This paper will lead to the development of early risk prediction and diagnosis tool of MACE in ACS patients.

Comorbidities associated with different levels of total cholesterol in male and female acute ischemic stroke patients

Men and women differ in their clinical risk factors with respect to various predictors of severity in acute ischemic stroke (AIS). High cholesterol is a risk factor for AIS and the mechanism by which high cholesterol levels lead to an AIS is well established. However, the specific relationship between demographic, clinical risk factors, total cholesterol, and the resulting gender difference in AIS patients is yet to be investigated. This study recruited AIS patients between January 2000 and June 2016 classified into normal, borderline or high total cholesterol (TC). Normal was defined as ≤200 mg/dl, borderline 200 to 239 mg/dl and high ≥240 mg/dl based on Adult Treatment Panel III (ATP III) Guidelines for the classification of TC levels. The logistic regression model was used to predict clinical risk factors associated within men and women AIS patients with different levels of TC. A total of 3532 AIS patients presented with normal TC, 760 patients with borderline TC and 427 patients with high TC. The high total cholesterol group was more likely to be women with increasing age (OR = 1.028, 95% CI, 1.006-1.052, P = .014), body mass index (BMI) (OR = 1.052, 95% CI, 1.004-1.102, P = .033), and high-density lipoprotein cholesterol (HDL-C) (OR = 1.039, 95% CI, 1.019-1.060, P < .001), while those with coronary artery disease (OR = 0.435, 95% CI, 0.234-0.809, P = .003), history of drug or alcohol abuse (OR = 0.261, 95% CI, 0.079-0.867, P = .028), increasing INR (OR = 0.187, 95% CI, 0.047-0.748, P = .018), and elevated diastolic blood pressure (OR = 0.982, 95% CI, 0.970-0.995, P = .006) were associated with being a male AIS patient. There were disparities in demographic and clinical risk factors associated with high TC levels in men when compared to women and more clinical risk factors were associated with high TC levels in men when compared to women with AIS. It is important to take into account specific clinical risk factors associated with gender-related differences in total cholesterol in AIS population to facilitate personalizing their therapeutic actions.