Background/Objectives: Automatic classification of ECG signal arrhythmias plays a vital role in early cardiovascular diagnostics by enabling prompt detection of life-threatening conditions. Manual ECG interpretation is labor-intensive and susceptible to errors, highlighting the demand for automated, scalable approaches. Deep learning (DL) methods are effective in ECG analysis due to their ability to learn complex patterns from raw signals. Methods: This study introduces two models: a custom convolutional neural network (CNN) with a dual-branch architecture for processing ECG signals and demographic data (e.g., age, gender), and a modified VGG16 model adapted for multi-branch input. Using the PTB-XL dataset, a widely adopted large-scale ECG database with over 20,000 recordings, the models were evaluated on binary, multiclass, and subclass classification tasks across 2, 5, 10, and 15 disease categories. Advanced preprocessing techniques, combined with demographic features, significantly enhanced performance. Results: The CNN model achieved up to 97.78% accuracy in binary classification and 79.7% in multiclass tasks, outperforming the VGG16 model (97.38% and 76.53%, respectively) and state-of-the-art benchmarks like CNN-LSTM and CNN entropy features. This study also emphasizes interpretability, providing lead-specific insights into ECG contributions to promote clinical transparency. Conclusions: These results confirm the models’ potential for accurate, explainable arrhythmia detection and their applicability in real-world healthcare diagnostics.

Pneumonia remains a serious global health issue, particularly affecting vulnerable groups such as children and the elderly, where timely and accurate diagnosis is critical for effective treatment. Recent advances in deep learning have significantly enhanced pneumonia detection using chest X-rays, yet many current methods still face challenges with interpretability, efficiency, and clinical applicability. In this work, we proposed a YOLOv11-based deep learning framework designed for real-time pneumonia detection, strengthened by the integration of Grad-CAM for visual interpretability. To further enhance robustness, the framework incorporated preprocessing techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE) for contrast improvement, region-of-interest extraction, and lung segmentation, ensuring both precise localization and improved focus on clinically relevant features. Evaluation on two publicly available datasets confirmed the effectiveness of the approach. On the COVID-19 Radiography Dataset, the system reached a macro-average accuracy of 98.50%, precision of 98.60%, recall of 97.40%, and F1-score of 97.99%. On the Chest X-ray COVID-19 & Pneumonia dataset, it achieved 98.06% accuracy, with corresponding high precision and recall, yielding an F1-score of 98.06%. The Grad-CAM visualizations consistently highlighted pathologically relevant lung regions, providing radiologists with interpretable and trustworthy predictions. Comparative analysis with other recent approaches demonstrated the superiority of the proposed method in both diagnostic accuracy and transparency. With its combination of real-time processing, strong predictive capability, and explainable outputs, the framework represents a reliable and clinically applicable tool for supporting pneumonia and COVID-19 diagnosis in diverse healthcare settings.

Background: Publicly reported celebrity deaths receive substantial media attention and may influence how individuals perceive disease burden. However, datasets constructed from publicly available sources are often curated and may not reflect underlying epidemiological distributions. Understanding how dataset construction influences observed mortality patterns is therefore important.

The increasing complexity of disability-related healthcare conditions requires intelligent systems capable of integrating heterogeneous physiological, behavioral, nutritional, demographic, and functional health information under evolving real-world conditions. Existing healthcare artificial intelligence systems frequently rely on centralized black-box architectures that suffer from limited interpretability, unstable generalization under temporal drift, and weak deployment scalability in resource-constrained environments. This study proposes a lightweight organizational healthcare intelligence framework for multidomain disability risk prediction using harmonized National Health and Nutrition Examination Survey (NHANES) data. Instead of monolithic prediction, the proposed framework organizes lightweight specialist learners into cooperative teams representing behavioral, physiological, nutritional–metabolic, demographic–fairness, and functional–disability domains. Adaptive coordination among specialist teams is achieved through Nash-cooperative optimization integrated with swarm-based strategies including Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony Optimization, and Harris Hawks Optimization. The framework additionally incorporates explainable specialist reasoning, lightweight edge-oriented inference, and temporal robustness evaluation under heterogeneous healthcare distributions. Experimental evaluation using pooled NHANES 2011-2018 data achieved a 73.90% classification accuracy, a weighted F1-score of 68.03%, a Matthews correlation coefficient of 0.6228, and a multiclass area under the receiver operating characteristic curve of 0.8905. Explainability analysis demonstrated heterogeneous specialist domain contributions, with physiological reasoning achieving the strongest independent predictive capability while demographic–fairness specialization preserved cooperative organizational stability. Swarm-adaptive coordination further improved balanced optimization behavior while maintaining lightweight inference latency near 0.01 ms/sample. External later-cycle evaluation under temporally shifted NHANES environments demonstrated preservation of higher-level disability risk organization despite substantial distribution drift. The proposed framework provides a scalable, interpretable, lightweight, and temporally adaptive organizational intelligence approach for disability-aware healthcare analytics.