Background/Objectives: Cardiovascular disease (CVD) remains a leading cause of mortality and disability worldwide, with timely diagnosis critical for preventing long-term functional impairment. Electrocardiograms (ECGs) provide essential biomarkers of cardiac function, but their interpretation is often complex, particularly across multi-institutional datasets. Methods: This study presents an explainable federated learning framework with long short-term memory (FL-LSTM) for multi-class heart disease classification, capable of distinguishing arrhythmia, ischemia, and healthy states while preserving patient privacy. Results: The model was trained and evaluated on three heterogeneous ECG datasets, achieving 92% accuracy, 99% AUC, and 91% F1 score, outperforming existing federated approaches. Model interpretability is provided via SHapley Additive exPlanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME), highlighting clinically relevant ECG biomarkers such as P-wave height, R-wave height, QRS complex, RR interval, and QT interval. Conclusions: By integrating temporal modeling, federated learning, and interpretable AI, the framework enables secure and collaborative cardiac diagnosis while supporting transparent clinical decision-making in distributed healthcare settings.

Background: Neurodevelopmental disorders (NDDs) are chronic disorders considered an emerging public health problem. This condition can markedly impair the Health-related Quality of Life (HRQoL). This study aimed to examine the HRQoL among children with NDDs. Design and methods: A cross-sectional study was conducted among 73 parents/caregivers of children with NDDs aged 2–12 years. Pediatric Quality of Life Inventory 4.0 Generic Core Scale was used to assess the HRQoL. A multiple linear regression model was performed to assess the association. Results: Among the 73 parents/caregivers, majority of their children fell into 2–4 year age group, with 70% being male. In contrast to other NDDs, this study highlights that the mean HRQoL scores for Cerebral palsy (CP), Down syndrome, Intellectual disability (ID), Motor disorders, and Neurogenetic disorders were below 50 on the 0–100 scale. The analysis of variance revealed a significant effect of NDD categories on the physical domain of QoL. Findings from regression analysis highlight that the physical functioning domain of QoL was significantly influenced by factors including age [ß: 2.44; 95%CI: (0.07, 4.81)], various NDDs, such as Attention-deficit hyperactivity disorder [ß: −25.54, 95%CI: (−43.12, −7.96)], Autism [ß: −25.44, 95%CI: (−41.51, −9.37)], CP [ß: −46.52, 95%CI: (−65.41, −17.62)], Down syndrome [ß: −42.33, 95%CI: (−63.28, −21.37)], ID [ß: −46.14, 95%CI: (−82.61, −9.67)] and motor disorder [ß: −77.07, 95%CI: (−103.32, −50.82)] and stable family income [ß: 16.37; 95%CI: (3.44, 29.29)]. This study also underscores that emotional functioning was significantly influenced by sex [ß: 18.27; 95%CI: (4.57, 31.97)], motor disorders [ß: −36.15, 95%CI: (−67.14, −5.16)], and social functioning was influenced by birth order 3rd/more [ß: 32.24;95%CI: (7.43, 57.04)]. Conclusions: This exploratory study highlights that multiple factors may influence various domains of HRQoL, including physical, emotional, social, and school functioning in children, providing a foundation for future studies.