Utilizing machine learning algorithms for the prediction of carotid artery plaques in a Chinese population

Development and validation of a carotid plaque risk prediction model for coal miners

Objective

Carotid plaque represents an independent risk factor for cardiovascular disease and a significant threat to human health. The aim of the study is to develop an accurate and interpretable predictive model for early detection the occurrence of carotid plaque.

Methods

A cross-sectional study was conducted by selecting coal miners who participated in medical examinations from October 2021 to January 2022 at a hospital in North China. The features were initially screened using extreme gradient boosting (XGBoost), random forest, and LASSO regression, and the model was subsequently constructed using logistic regression. The three models were then compared, and the optimum model was identified. Finally, a nomogram was plotted to increase its interpretability.

Results

The XGBoost algorithm demonstrated superior performance in feature screening, identifying the top five features as follows: age, systolic blood pressure, low-density lipoprotein cholesterol, white blood cell count, and body mass index (BMI). The area under the curve (AUC), sensitivity, and specificity of the model constructed based on the XGBoost algorithm were 0.846, 0.867, and 0.702, respectively.

Conclusions

It is possible to predict the presence of carotid plaque using machine learning. The model has high application value and can better predict the risk of carotid artery plaque in coal miners. Furthermore, it provides a theoretical basis for the health management of coal miners.

Combining Computational Fluid Dynamics, Structural Analysis, and Machine Learning to Predict Cerebrovascular Events: A Mild ML Approach

Background/Objectives: Cerebrovascular events, such as strokes, are often preceded by the rupture of atherosclerotic plaques in the carotid arteries. This work introduces a novel approach to predict the occurrence of such events by integrating computational fluid dynamics (CFD), structural analysis, and machine learning (ML) techniques. The objective is to develop a predictive model that combines both imaging and non-imaging data to assess the risk of carotid atherosclerosis and subsequent cerebrovascular events, ultimately improving clinical decision-making. Methods: A multidisciplinary approach was employed, utilizing 3D reconstruction techniques and blood-flow simulations to extract key plaque characteristics. These were combined with patient-specific clinical data for risk evaluation. The study involved 134 asymptomatic individuals diagnosed with carotid artery disease. Data imbalance was addressed using two distinct approaches, with the optimal method chosen for training a Gradient Boosting Tree (GBT) classifier. The model's performance was evaluated in terms of accuracy, sensitivity, specificity, and ROC AUC. Results: The best-performing GBT model achieved a balanced accuracy of 88%, with a ROC AUC of 0.92, a sensitivity of 0.88, and a specificity of 0.91. This demonstrates the model's high predictive power in identifying patients at risk for cerebrovascular events. Conclusions: The proposed method effectively combines CFD, structural analysis, and ML to predict cerebrovascular event risk in patients with carotid artery disease. By providing clinicians with a tool for better risk assessment, this approach has the potential to significantly enhance clinical decision-making and patient outcomes.

Using machine learning-based algorithms to construct cardiovascular risk prediction models for Taiwanese adults based on traditional and novel risk factors

OBJECTIVE

To develop and validate machine learning models for predicting coronary artery disease (CAD) within a Taiwanese cohort, with an emphasis on identifying significant predictors and comparing the performance of various models.

METHODS

This study involved a comprehensive analysis of clinical, demographic, and laboratory data from 8,495 subjects in Taiwan Biobank (TWB) after propensity score matching to address potential confounding factors. Key variables included age, gender, lipid profiles (T-CHO, HDL_C, LDL_C, TG), smoking and alcohol consumption habits, and renal and liver function markers. The performance of multiple machine learning models was evaluated.

RESULTS

The cohort comprised 1,699 individuals with CAD identified through self-reported questionnaires. Significant differences were observed between CAD and non-CAD individuals regarding demographics and clinical features. Notably, the Gradient Boosting model emerged as the most accurate, achieving an AUC of 0.846 (95% confidence interval [CI] 0.819-0.873), sensitivity of 0.776 (95% CI, 0.732-0.820), and specificity of 0.759 (95% CI, 0.736-0.782), respectively. The accuracy was 0.762 (95% CI, 0.742-0.782). Age was identified as the most influential predictor of CAD risk within the studied dataset.

CONCLUSION

The Gradient Boosting machine learning model demonstrated superior performance in predicting CAD within the Taiwanese cohort, with age being a critical predictor. These findings underscore the potential of machine learning models in enhancing the prediction accuracy of CAD, thereby supporting early detection and targeted intervention strategies.

TRIAL REGISTRATION

Not applicable.

Integrated Bioinformatics and Machine Learning Analysis Identify ACADL as a Potent Biomarker of Reactive Mesothelial Cells

Mesothelial cells with reactive hyperplasia are difficult to distinguish from malignant mesothelioma cells based on cell morphology. This study aimed to identify and validate potential biomarkers that distinguish mesothelial cells from mesothelioma cells through machine learning combined with immunohistochemistry. It integrated the gene expression matrix from three Gene Expression Omnibus data sets (GSE2549, GSE12345, and GSE51024) to analyze the differently expressed genes between normal and mesothelioma tissues. Then, three machine learning algorithms, least absolute shrinkage and selection operator, support vector machine recursive feature elimination, and random forest were used to screen and obtain four shared candidate markers, including ACADL, EMP2, GPD1L, and HMMR. The receiver operating characteristic curve analysis showed that the area under the curve for distinguishing normal mesothelial cells from mesothelioma was 0.976, 0.943, 0.962, and 0.956, respectively. The expression and diagnostic performance of these candidate genes were validated in two additional independent data sets (GSE42977 and GSE112154), indicating that the performances of ACADL, GPD1L, and HMMR were consistent between the training and validation data sets. Finally, the optimal candidate marker ACADL was verified by immunohistochemistry assay. Acyl-CoA dehydrogenase long chain (ACADL) was stained strongly in mesothelial cells, especially for reactive hyperplasic mesothelial cells, but was negative in malignant mesothelioma cells. Therefore, ACADL has the potential to be used as a specific marker of reactive hyperplasic mesothelial cells in the differential diagnosis of mesothelioma.