Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion

Stacking learning based on micro-CT radiomics for outcome prediction in the early-stage of silica-induced pulmonary fibrosis model

Silicosis is a progressive pulmonary fibrosis disease caused by long-term inhalation of silica. The early diagnosis and timely implementation of intervention measures are crucial in preventing silicosis deterioration further. However, the lack of screening and diagnostic measures for early-stage silicosis remains a significant challenge. In this study, silicosis models of varying severity were established through a single exposure to silica with different doses (2.5mg/mice or 5mg/mice) and durations (4 weeks or 12 weeks). The diagnostic performance of computed tomography (CT) quantitative analysis was assessed using lung density biomarkers and the lung density distribution histogram, with a particular focus on non-aerated lung volume. Subsequently, we developed and evaluated a stacking learning model for early diagnosis of silicosis after extracting and selecting features from CT images. The CT quantitative analysis reveals that while the lung densitometric biomarkers and lung density distribution histogram, as traditional indicators, effectively differentiate severe fibrosis models, they are unable to distinguish early-stage silicosis. Furthermore, these findings remained consistent even when employing non-aerated areas, which is a more sensitive indicator. By establishing a radiomics stacking learning model based on non-aerated areas, we can achieve remarkable diagnostic performance to distinguish early-stage silicosis, which can provide a valuable tool for clinical assistant diagnosis. This study reveals the potential of using non-aerated lung areas as a region of interest in stacking learning for early diagnosis of silicosis, providing new insights into early detection of this disease.

Cardiovascular disease detection using a novel stack-based ensemble classifier with aggregation layer, DOWA operator, and feature transformation

Due to their widespread prevalence and impact on quality of life, cardiovascular diseases (CVD) pose a considerable global health burden. Early detection and intervention can reduce the incidence, severity, and progression of CVD and prevent premature death. The application of machine learning (ML) techniques to early CVD detection is therefore a valuable approach. In this paper, A stack-based ensemble classifier with an aggregation layer and the dependent ordered weighted averaging (DOWA) operator is proposed for detecting cardiovascular diseases. We propose transforming features using the Johnson transformation technique and normalizing feature distributions. Three diverse first-level classifiers are selected based on their accuracy, and predictions are combined using the aggregation layer and DOWA. A linear support vector machine (SVM) meta-classifier makes the final classification. Adding the aggregation layer to the stacking classifier improves classification accuracy significantly, according to the study. The accuracy is enhanced by 5%, resulting in an impressive overall accuracy of 94.05%. Moreover, the proposed system significantly increases the area under the receiver operating characteristic (ROC) curve compared to recent studies, reaching 97.14%. It further reinforces the classifier's reliability and effectiveness in classifying cardiovascular disease by distinguishing between positive and negative instances. With improved accuracy and a high area under the curve (AUC), the proposed classifier exhibits robustness and superior performance in the detection of cardiovascular diseases.

Union With Recursive Feature Elimination: A Feature Selection Framework to Improve the Classification Performance of Multicategory Causes of Death in Colorectal Cancer

Despite the use of machine learning tools, it is challenging to properly model cause-specific deaths in colorectal cancer (CRC) patients and choose appropriate treatments. Here, we propose an interesting feature selection framework, namely union with recursive feature elimination (U-RFE), to select the union feature sets that are crucial in CRC progression-specific mortality using The Cancer Genome Atlas (TCGA) dataset. Based on the union feature sets, we compared the performance of 5 classification algorithms, including logistic regression (LR), support vector machines (SVM), random forest (RF), eXtreme gradient boosting (XGBoost), and Stacking, to identify the best model for classifying 4-category deaths. In the first stage of U-RFE, LR, SVM, and RF were used as base estimators to obtain subsets containing the same number of features but not exactly the same specific features. Union analysis of the subsets was then performed to determine the final union feature set, effectively combining the advantages of different algorithms. We found that the U-RFE framework could improve various models' performance. Stacking outperformed LR, SVM, RF, and XGBoost in most scenarios. When the target feature number of the RFE was set to 50 and the union feature set contained 298 deterministic features, the Stacking model achieved F1_weighted, Recall_weighted, Precision_weighted, Accuracy, and Matthews correlation coefficient of 0.851, 0.864, 0.854, 0.864, and 0.717, respectively. The performance of the minority categories was also significantly improved. Therefore, this recursive feature elimination-based approach of feature selection improves performances of classifying CRC deaths using clinical and omics data or those using other data with high feature redundancy and imbalance.

Special Issue on Recent Advances in Machine Learning and Applications

Digital technologies for cyber-physical systems are rapidly advancing, and the ubiquity of the Internet of Things (IoT) has created significant challenges for academic, industrial, and service applications due to high dimensionality, noise contamination, incompleteness, inconsistency, and massive amounts of data [...]