The purpose of this study is to prioritize key environmental sustainability dimensions and indicators within the pharmaceutical sector to support informed decision-making and policy formulation. The study adopts a quantitative methodology using RIDIT analysis to compare multiple sustainability indicators against a reference distribution and derive priority rankings. Survey data collected from 516 respondents were analyzed across environmental impact, environmental sustainability, impact investing, and adoption of green pharmaceutical practices. The findings reveal that environmental sustainability and environmental impact indicators receive the highest priority, indicating strong stakeholder emphasis on long-term ecological outcomes. Impact investing plays a moderate facilitative role, while green practice adoption ranks lowest, highlighting implementation gaps. The study offers practical implications for policymakers and industry managers by identifying areas requiring targeted interventions and resource allocation. The novelty of this research lies in the application of RIDIT analysis to systematically rank sustainability dimensions in the pharmaceutical context, providing a robust and interpretable prioritization framework.

The rapid growth of healthcare information systems has led to an increase in the volume of clinical data stored in electronic medical record systems. This data contains valuable information such as patient demographics, vital signs, diagnoses, and medical procedures, which can be used to support clinical decision-making. Determining the appropriate clinical procedure remains a challenge due to the complexity of patient conditions and the potential need for multiple procedures simultaneously. This study aims to develop a robust model for predicting clinical procedures using a multi-label classification approach based on electronic medical record data. The proposed methodology integrates data preprocessing, feature engineering, followed by the implementation of several Machine Learning methods such as LightGBM, XGBoost, CatBoost, Deep Learning Models and combined using an Ensemble Learning method. Various Ensemble Learning techniques, including Simple Average, Machine Learning Average, and Optimized Weight-based combinations, are applied to improve predictive performance. Evaluation is performed using the Micro F1 score and Macro F1 score to assess overall performance. Experimental results show that individual models achieve competitive performance, with LightGBM, XGBoost, CatBoost outperforming the deep learning approach on tabular clinical data. The Ensemble Learning approach further improved performance, with the Optimized Weight with Grid Search Optimization ensemble achieving the highest Micro F1 score of 0.7788 and Macro F1 score of 0.7674. These results also demonstrate that combining multiple models effectively reduces bias and variance while improving generalization. The small difference between Micro and Macro F1 scores indicates balanced performance across labels. In conclusion, the proposed Ensemble Learning-based multi-label prediction model demonstrates robust capabilities in handling complex clinical data and improving prediction accuracy. This study highlights the importance of model combination strategies rather than increasing model complexity. These findings have practical implications for supporting clinical decision-making systems in healthcare settings.

Despite the growing adoption of artificial intelligence (AI) in healthcare, improving AI frameworks remains essential for effective medical implementation. A major challenge in developing reliable machine learning models is handling imbalanced medical datasets. Although metaheuristic algorithms can improve machine learning performance, their effectiveness on imbalanced medical data has received limited attention. This study evaluates four nature-inspired optimization techniques, bat algorithm (BA), genetic algorithm (GA), improved grey wolf optimization (IGWO), and improved whale optimization (IWO), for enhancing balanced random forest (BRF) classification on imbalanced datasets involving breast tumors, heart failure survival, mammogram microcalcification detection, and diabetes. Based on F1 scores, IGWO achieved the greatest improvements (0.256–0.060), followed by IWO (0.229–0.045), GA (0.214–0.036), and BA (0.203–0.036). Validation results confirmed IGWO’s strong convergence and optimization efficiency, consistently outperforming the other methods. The best performance was achieved by IGWO-BRF in breast cancer diagnosis (F1 = 1.000), while the lowest was observed for BA-BRF in mammogram microcalcification detection (F1 = 0.570). Optimization time depended on both the search strategy and dataset characteristics, with IWO showing the fastest optimization across all datasets. Overall, nature-inspired optimizers significantly improved classification performance on imbalanced medical datasets.

Antenatal care (ANC) is a key determinant of maternal and neonatal health, yet its utilization remains uneven across India. This study examined the socio-demographic determinants of receiving the recommended four or more ANC visits using data from 232,920 women aged 15–49 years from the National Family Health Survey (NFHS-5). Descriptive statistics and multivariable logistic regression were used to identify factors associated with adequate ANC utilization. Overall, 57.9% of women received four or more ANC visits. Higher maternal education, better household wealth, husband's education, and regular mass media exposure significantly increased the likelihood of adequate ANC utilization, whereas rural residence was associated with lower utilization. Women aged 25–39 years were also more likely to complete the recommended ANC visits than younger women. The findings highlight persistent socio-economic and geographical disparities in maternal healthcare utilization and emphasize the need for targeted interventions focusing on education, rural healthcare access, economic empowerment, and health awareness to improve equitable ANC coverage in India.

The process of navigating medical insurance markets, particularly within emerging economies characterized by financial volatility, has remained a significant hurdle for individual decision-makers. This study addresses the administrative complexity and cognitive burden inherent in selecting a medical insurance provider by transitioning the process from an intuitive task into a mathematically rigorous engineering decision solution. Utilizing a mixed-methods design, we developed a localized decision support system (DSS) in Python that integrates binary matchmaking logic with multi-criteria weighted scoring. This hybridized approach effectively handles the all-or-nothing nature of healthcare benefits that traditional models often struggle to resolve. Empirical justification for the system is anchored in a survey of 74 professionals within the case market, revealing a systemic mismatched budget paradox. The results revealed statistically significant negative correlations between income and healthcare risk drivers, such as age and family size, necessitating a recommendation engine capable of nuanced trade-off analysis. Validation results indicate that 80% of users experienced a marked improvement in decisional satisfaction and suitability. By prioritizing explainable AI over proprietary black-box systems, this study provides a scalable framework to mitigate informational asymmetry and financial risk, ensuring that healthcare access is dictated by personalized need rather than administrative complexity.