Cardiovascular disease remains the leading cause of mortality worldwide, driving substantial research investment and necessitating robust evaluation of institutional research performance. This study employed the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to evaluate and rank the research performance of 30 leading universities based on their contributions to the top 100 highly cited cardiovascular medicine articles from 2010–2026, extracted from Scopus. Three evaluation criteria were selected: Unique Authors (minimized as a resource input measure), Total Citations (maximized as an impact indicator), and Number of Papers (maximized as a productivity measure), with equal weights (33.3% each) assigned to all criteria. TOPSIS analysis revealed substantial variation in institutional performance, with Harvard University emerging as the top-performing institution (TOPSIS score: 0.6050), demonstrating dominance across all criteria with 228 unique authors, 9,430 citations, and 83 papers, followed by Cleveland Clinic (0.4882), University of Toronto (0.4537), and Mayo Clinic (0.4158). US institutions dominated the ranking, accounting for 27 of 30 universities with an average TOPSIS score of 0.4112, while Canadian institutions achieved an average of 0.4078. The analysis identified an “efficiency gap” between high-citation, low-paper institutions (e.g., UCSF with 87.4 citations per paper and 1.3 authors per paper) and those demonstrating the inverse pattern. The findings highlight the dominance of US institutions, varying institutional strategies (high-volume vs. high-impact approaches), and the concentration of research activity among leading academic medical centers, with the TOPSIS methodology offering a robust framework for multidimensional research evaluation that can inform institutional benchmarking, strategic planning, and resource allocation in cardiovascular medicine.

Health performance in India varies markedly across states and Union Territories, yet composite rankings that summarise this variation are sensitive to how criteria are weighted and how alternatives are aggregated. Subjective weighting and single-method aggregation limit the transparency and reproducibility of existing rankings. This study constructs an objective, reproducible composite ranking of subnational health-system performance using the sixth round of the National Family Health Survey (NFHS-6, 2023-24). Thirty-five states and Union Territories were evaluated on twenty-one curated headline indicators spanning six analytical domains (marriage and fertility, maternal health, child health, child nutrition, adult nutrition and non-communicable disease risk, and women's empowerment). Benefit, cost, and target-based criteria were handled coherently; the two target criteria (total fertility rate and caesarean section rate) were transformed to distance from their clinical optimum. Objective weights were derived with the Method based on the Removal Effects of Criteria (MEREC). Alternatives were ranked with the Alternative Ranking Order Method Accounting for Two-step Normalization (AROMAN), implemented with type-aware normalization. Robustness was assessed through weight perturbation, aggregation-parameter sweeps, an all-eligible-criteria variant, and an imputation sensitivity analysis. Convergent validity was examined against four independent ranking methods, and external validity against independent socioeconomic measures. The two fertility-related target criteria and child marriage received the largest MEREC weights (0.103, 0.099, and 0.075, respectively). Ladakh, Himachal Pradesh, and Mizoram occupied the top three positions, whereas Bihar, Uttar Pradesh, and Tripura occupied the bottom three. The ranking was highly stable: the mean Spearman correlation between the base ranking and 5000 randomly perturbed weight vectors was 0.99, and rank correlations across the aggregation parameter exceeded 0.93. Agreement with the four comparison methods was strong at the policy-relevant extremes (WS rank-similarity coefficient between 0.91 and 0.96) and moderate to strong across the full ordering (Spearman correlation between 0.49 and 0.83). The composite score correlated positively with the socioeconomic enabling environment (Spearman correlation 0.69, p ≤0.001). The integrated MEREC-AROMAN framework produces a transparent and reproducible ranking of Indian subnational health performance that is stable under perturbation and consistent with independent evidence. The persistent disadvantage of the large northern states, alongside the emerging burden of over-medicalisation and adult obesity in the southern states, indicates that a single national policy prescription is unlikely to be adequate.

The Cardiac Health Assessment System for vehicle drivers utilizes the synergy and combined effect arising from the integration of Machine Learning, Internet of Things that will allow continuous monitoring and analysis of the driver's cardiovascular well-being. The current system Primarily consists of detecting Cardiac Arrhythmia, is founded on traditional Electrocardiogram (ECG) monitoring, which requires electrodes being placed on the skin of the patients to register the electrical activities. With the use of smart wearables and ECG sensors, the system can register the activities in real time heart health data when the person is driving and when not driving. With the use of Internet of Things (IoT), the details are seamlessly communicated to a central system. Sophisticated Machine Learning algorithms evaluate these ECG patterns to provide insights to the early detection of any irregularities and risks. The conventional ECG devices are not coupled with the Internet of Things. sensors, limiting the scope of further necessary data, like age, sex, height, and weight. In the case of irregularities, the system provides immediate notifications, helping towards quick intervention and, if required, remote medical consultations. The friendly interface of this system gives drivers access to their health data, as well as personalized suggestions, to cultivate an active health care strategy This unique innovation did not occur in isolation but only improves road safety as far as indicating possible cardiac problems but also helps in creating a data-driven the level of understanding pertaining to the relationship between cardiovascular well-being and driving factors. This research work describes the design of an Internet of Things -assisted ECG monitoring and heart disease diagnosis system specifically for vehicles. The system can acquire real-time ECG signals from wearable biosensors and transmit them. to a cloud platform by means of low-power Internet of Thing modules. The vehicle-monitoring system offers immediate notification to all drivers and subscribed stakeholders when abnormal patterns emerge, improving safety on the roads and allowing for immediate medical intervention. Thus, this concept promotes continuous physiological surveillance, fast risk analysis, and preventive measures for heart-related incidents during driving.

Wearable and Internet of Medical Things devices increasingly support continuous health monitoring, but cloud-dependent analytics remain constrained by latency, connectivity, privacy, and battery requirements. Tiny machine learning shifts inference toward resource-constrained microcontrollers and edge processors; however, its clinical value depends not only on predictive accuracy but also on energy efficiency, real-time responsiveness, and understandable decision logic. This scoping review mapped the evidence on explainability and energy efficiency in TinyML-based health monitoring. Following the Joanna Briggs Institute approach and PRISMA guidance for scoping reviews, Scopus was searched in the title, abstract, and keyword fields for studies published from 2020 to 2025. The search combined TinyML and embedded or edge artificial intelligence terms with wearable or Internet of Medical Things concepts, health-monitoring applications, and explainability or efficiency terms. The supplied export contained 293 records. After screening, 39 reports underwent eligibility assessment and 36 studies were included. Publication activity accelerated sharply, with 18 studies published in 2025. The evidence covered cardiac monitoring, human activity and fall detection, neurological and affective assessment, respiratory monitoring, signal-quality control, glucose sensing, gait analysis, and smart textiles. Convolutional neural networks and hybrid deep models were common, while deployment platforms ranged from microcontrollers to field-programmable gate arrays, application-specific integrated circuits, and neuromorphic hardware. Quantization, pruning, binary or ternary computation, feature reduction, event-driven processing, and local transmission control were frequently used to reduce resource demand. In contrast, only a small minority of studies explicitly evaluated explainability through model-based feature selection, feature importance, or class activation maps. The field is therefore energy-aware but not yet consistently explanation-aware. Future research should adopt standardized hardware reporting, clinician-centered explanation evaluation, external and longitudinal validation, and multiobjective optimization that jointly considers clinical accuracy, energy, latency, memory, robustness, and interpretability.

Healthcare distribution companies generate large volumes of transaction data that can support product-combination decisions. However, conventional Market Basket Analysis often relies on support-based measures, which prioritize frequently purchased products without considering their economic contribution. This study proposes a revenue-based High-Utility Itemset Mining framework to identify economically valuable product combinations from healthcare distribution transaction data. The proposed approach uses transaction-level quantity, unit selling price, discount, and realized line-level transaction value to calculate product utility. FP-Growth is applied as a baseline support-based method, while High-Utility Itemset Mining is used to discover product combinations that exceed a minimum utility threshold. The empirical results show that support-based and utility-based mining produce substantially different insights. Several products with high support contribute relatively low utility, while some lower-support itemsets generate high utility shares. At the 1.00% minimum utility threshold, the proposed framework identifies 484 high-utility itemsets after evaluating 12,660 candidate itemsets and pruning 9,192 candidates using Transaction-Weighted Utility. The findings support cross-selling, product bundling, and inventory prioritization decisions. This study demonstrates that utility-based mining provides more economically relevant product-combination insights than frequency-based Market Basket Analysis alone.