Prediction of atherosclerotic disease progression combining computational modelling with machine learning

Machine learning approaches for biomarker discovery to predict large-artery atherosclerosis

Large-artery atherosclerosis (LAA) is a leading cause of cerebrovascular disease. However, LAA diagnosis is costly and needs professional identification. Many metabolites have been identified as biomarkers of specific traits. However, there are inconsistent findings regarding suitable biomarkers for the prediction of LAA. In this study, we propose a new method integrates multiple machine learning algorithms and feature selection method to handle multidimensional data. Among the six machine learning models, logistic regression (LR) model exhibited the best prediction performance. The value of area under the receiver operating characteristic curve (AUC) was 0.92 when 62 features were incorporated in the external validation set for the LR model. In this model, LAA could be well predicted by clinical risk factors including body mass index, smoking, and medications for controlling diabetes, hypertension, and hyperlipidemia as well as metabolites involved in aminoacyl-tRNA biosynthesis and lipid metabolism. In addition, we found that 27 features were present among the five adopted models that could provide good results. If these 27 features were used in the LR model, an AUC value of 0.93 could be achieved. Our study has demonstrated the effectiveness of combining machine learning algorithms with recursive feature elimination and cross-validation methods for biomarker identification. Moreover, we have shown that using shared features can yield more reliable correlations than either model, which can be valuable for future identification of LAA.

Prediction of atherosclerosis using machine learning based on operations research

BACKGROUND

Atherosclerosis is one of the major reasons for cardiovascular disease including coronary heart disease, cerebral infarction and peripheral vascular disease. Atherosclerosis has no obvious symptoms in its early stages, so the key to the treatment of atherosclerosis is early intervention of risk factors. Machine learning methods have been used to predict atherosclerosis, but the presence of strong causal relationships between features can lead to extremely high levels of information redundancy, which can affect the effectiveness of prediction systems.

OBJECTIVE

We aim to combine statistical analysis and machine learning methods to reduce information redundancy and further improve the accuracy of disease diagnosis.

METHODS

We cleaned and collated the relevant data obtained from the retrospective study at Affiliated Hospital of Nanjing University of Chinese Medicine through data analysis. First, some features that with too many missing values are filtered out of the 34 features, leaving 25 features. 49% of the samples were categorized as the atherosclerosis risk group while the rest 51% as the control group without atherosclerosis risk under the guidance of relevant experts. We compared the prediction results of a single indicator that had been medically proven to be highly correlated with atherosclerosis with the prediction results of multiple features to fully demonstrate the effect of feature information redundancy on the prediction results. Then the features that could distinguish whether have atherosclerosis risk or not were retained by statistical tests, leaving 20 features. To reduce the information redundancy between features, after drawing inspiration from graph theory, machine learning combined with optimal correlation distances was then used to screen out 15 significant features, and the prediction models were evaluated under the 15 features. Finally, the information of the 5 screened-out non-significant features was fully utilized by ensemble learning to improve the prediction superiority for atherosclerosis.

RESULTS

Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), which is used to measure the predictive performance of the model, was 0.84035 and Kolmogorov-Smirnov (KS) value was 0.646. After feature selection model based on optimal correlation distance, the AUC value was 0.88268 and the KS value was 0.688, both of which were improved by about 0.04. Finally, after ensemble learning, the AUC value of the model was further improved by 0.01369 to 0.89637.

CONCLUSIONS

The optimal distance feature screening model proposed in this paper improves the performance of atherosclerosis prediction models in terms of both prediction accuracy and AUC metrics. Code and models are available at https://github.com/Cesartwothousands/Prediction-of-Atherosclerosis.