Background: Cardiovascular disease (CVD) is a prominent determinant of mortality, accounting for 17 million lives lost across the globe each year. This underscores its severity as a critical health issue. Extensive research has been undertaken to refine the forecasting of CVD in patients using various supervised, unsupervised, and deep learning approaches. Methods: This study presents HeartEnsembleNet, a novel hybrid ensemble learning model that integrates multiple machine learning (ML) classifiers for CVD risk assessment. The model is evaluated against six classical ML classifiers, including support vector machine (SVM), gradient boosting (GB), decision tree (DT), logistic regression (LR), k-nearest neighbor (KNN), and random forest (RF). Additionally, we compare HeartEnsembleNet with Hybrid Random Forest Linear Models (HRFLM) and ensemble techniques including stacking and voting. Results: Employing a dataset of 70,000 cardiac patients with 12 clinical attributes, our proposed model achieves a notable accuracy of 92.95% and a precision of 93.08%. Conclusions: These results highlight the effectiveness of hybrid ensemble learning in enhancing CVD risk prediction, offering a promising framework for clinical decision support.

Background/Objective: Chronic kidney disease (CKD) is a major global health concern associated with significant morbidity, mortality, and healthcare burden. This study aimed to develop an explainable machine learning framework that integrates lifestyle, sociodemographic, and biochemical factors for early CKD risk prediction among middle-aged men using public health survey data. Methods: Data from 968 male participants were preprocessed by removing missing values, deriving eGFR and ACR, and labeling CKD status. Five machine learning algorithms, (i.e., Random Forest, AdaBoost, Naïve Bayes, SVM, and XGBoost) were trained and evaluated using accuracy, precision, recall, and F1-score. Model interpretability was assessed using SHAP, LIME, Boruta, and Pearson’s correlation analyses. Results: AdaBoost yielded the best performance (accuracy = 0.7258, F1 = 0.6457, recall = 0.6923), with robust generalization confirmed by the precision–recall curve (AP = 0.715). SHAP and LIME revealed that serum creatinine, blood urea nitrogen, urinary creatinine, and age were major predictors, whereas lifestyle and metabolic indicators such as BMI, sodium and sugar intake, and sleep duration emerged as secondary factors for CKD. Conclusions: This study demonstrates the effectiveness of an explainable machine learning model that integrates lifestyle, sociodemographic and biochemical data for early CKD prediction among middle-aged men. The AdaBoost-based framework shows strong potential for implementation as a clinical decision-support tool within EHR systems and may contribute to personalized and preventive interventions. It emphasizes the growing importance of modifiable behaviors in kidney disease development and supports future work involving multiple cohorts and temporal model expansion to improve risk stratification for individuals at risk of kidney disease.

Background: Recently, the adoption of AI-based technologies has been accelerating in the field of medical image analysis. For the early diagnosis and treatment planning of breast cancer, Automated Breast Ultrasound (ABUS) has emerged as a safe and non-invasive imaging method, especially for women with dense breasts. However, the increasing computational cost due to the minute size and complexity of 3D ABUS data remains a major challenge. Methods: In this study, we propose a novel model based on the Mamba state–space model architecture for 3D tumor segmentation in ABUS images. The model uses Mamba blocks to effectively capture the volumetric spatial features of tumors, and integrates a deep spatial pyramid pooling (DASPP) module to extract multiscale contextual information from lesions of different sizes. Results: On the TDSC-2023 ABUS dataset, the proposed model achieved a Dice Similarity Coefficient (DSC) of 0.8062, and Intersection over Union (IoU) of 0.6831, using only 3.08 million parameters. Conclusions: These results show that the proposed model improves the performance of tumor segmentation in ABUS, offering both diagnostic precision and computational efficiency. The reduced computational space suggests a strong potential for real-world medical applications, where accurate early diagnosis can reduce costs and improve patient survival.