Anemia is a prevalent global health issue that is characterized by a deficit in red blood cells or low levels of hemoglobin. This condition is influenced by various causes, including nutritional inadequacies, chronic diseases, and genetic predisposition. The incidence of the phenomenon exhibits variation across different geographical regions and demographic groups. This pioneering research investigates the identification and classification of anemia, potentially leading to transformative advancements in the discipline. The classification of anemia encompasses four distinct groups, namely Beta Thalassemia Trait, Iron Deficiency Anemia, Hemoglobin E, and Combination. This comprehensive categorization offers clinicians a more refined and detailed comprehension of the condition. The integration of deep learning and machine learning in the Multilayer Extremely Randomized Tree Learning Machine (MERTLM) model represents a departure from traditional approaches and a significant advancement in the field of medical categorization accuracy. The MERTLM approach integrates randomized tree with multilayer extreme learning machine (M-ELM) representation learning, hence emphasizing the possibility of interdisciplinary collaboration in the field of diagnostics. In addition to its impact on anemia, artificial intelligence (AI) is playing a significant role in revolutionizing medical diagnosis by emphasizing the integration of innovative methods. This study utilizes the combined capabilities of machine learning and deep learning to improve accuracy. Notably, recent developments have resulted in an exceptional accuracy rate of 99.67%, precision of 99.60%, sensitivity of 99.47%, and an amazing F1-Score of 99.53%. This study represents a significant advancement in the field of anemia research, providing valuable insights that may be applied to intricate medical issues and enhancing the quality of patient care.

Since 2014, the World Health Organization has accumulated data indicating that 8.5% of 18-year-olds and older have been diagnosed with diabetes. In 2019, diabetes caused the lives of 1.5 million people worldwide, with those under the age of 70 accounting for 48% of all diabetes-related deaths. It is estimated that diabetes causes an additional 460,000 deaths each year due to renal failure and that hyperglycemia contributes to about 20% of all cardiovascular disease-related deaths. Diabetes may have contributed to a 3% rise in the age-adjusted death rate between the years 2000 and 2019. In recent years, the fatality rate attributable to diabetes has increased by 13% in low- and middle-income countries. Statistics collected by the World Health Organization indicate that the number of persons diagnosed with diabetes has increased from 108 million in 1980 to 422 million in 2014. The objective of this study is to construct a model capable of diagnosing persons with diabetes reliably, correctly, and consistently. This research used secondary data offered by Kaggle. The original data came from the National Institute of Diabetes and Digestive and Kidney Diseases. Each of the up to 768 data points consists of nine characteristics and two outputs, such as diabetes and non-diabetes in the provided example. In this study, a single algorithm is constructed by integrating two separate algorithms. Random forest algorithms, which are based on machine learning, and extreme learning machines, which are based on deep learning, have generated extraordinarily accurate results. When the confusion matrix is used, 98.05% accuracy is attained. Therefore, it is feasible to conclude that the suggested method was successful in completing an adequate analysis and classifying the data.

Demographic shifts resulting from population aging are evident globally, including in Southeast Asia, Thailand, and Indonesia. The relevance of advance care plans is becoming increasingly apparent as the worldwide demographic transforms due to aging. This study sought to investigate the factors influencing the use and implementation of advance care plans (ACPs) using the health belief model (HBM) and technology acceptance model (TAM). This study selected a sample of individuals aged 30–60 in Indonesia and Thailand based on established inclusion and exclusion criteria. The study utilized the purposive random sampling method, integrating aspects of purposive and random selection. A total of 532 questionnaires were distributed via an online form, and 472 were obtained after data cleaning. Most respondents to this survey came from Indonesia, comprising 238 out of 472 respondents (50.4%), and from Thailand, comprising 49.5%; most respondents were women who were between 36 and 40 years old, and most reported graduating with a bachelor’s degree. A significant construct influences the use of advance care plans, i.e., perceived barriers. The perceived barrier (PBA) construct included data security, accessibility, and language barriers. In summary, overcoming existing barriers can indirectly increase the benefits of advance care plans. The results show that perceived usefulness (β = 0.189, p < 0.001), perceived ease of use (β = 0.150, p < 0.01), perceived susceptibility (β = 0.153, p < 0.01), perceived severity (β = 0.105, p < 0.05), and perceived benefits (β = 0.241, p < 0.001) all had significant positive effects on behavioral intention. In contrast, health motivation (β = 0.073, p = 0.100) and perceived barriers (β = 0.034, p = 0.134) did not show significant relationships with behavioral intention in Indonesia and Thailand, offering insights into both countries’ development strategies and the promotion of advance care plans with media as the technology.

The increasing complexity of healthcare systems has led to a growing need for Advance Care Planning (ACP) to ensure personalized care for patients. Explainable Artificial Intelligence (XAI) has emerged as a promising solution to enhance ACP by providing transparent and interpretable decision-making processes. However, the current landscape of XAI in ACP remains unclear, necessitating a comprehensive bibliometric analysis. This study employed a systematic review of existing literature on XAI in ACP, using a bibliometric approach to analyze publication trends, collaboration patterns, and research themes. One hundred sixty articles were selected from prominent databases, and their metadata were extracted and analyzed using Biblioshiny, the analysis revealed a significant growth in ACP XAI-related publications, focusing on deep learning and natural language processing techniques. The top contributing authors and institutions were identified, and their collaborative networks were visualized. The results also highlighted the prominent themes of patient-centered care, decision support systems, and healthcare analytics. The study's findings have implications for developing more effective XAI-based ACP systems. This bibliometric analysis provides valuable insights into the current state of XAI in ACP, highlighting the need for further research and collaboration to address the complex challenges in healthcare. The study's outcomes can inform policymakers, researchers, and practitioners in developing more effective ACP systems that leverage the potential of XAI.

The intersection of behavioral economics (BE) and artificial intelligence (AI) has grown rapidly, yet its intellectual structure remains unmapped. This study aims to provide a bibliometric mapping of the BE-AI research field, identifying key themes, influential works, and research gaps. Using bibliometric analysis with the bibliometrix R-package (version 4.1) on 252 documents from Scopus (2006–2025), we performed performance analysis, co-citation, co-word, and collaboration network analysis. Annual output grows at 16.76%, with a surge since 2018. Loewenstein and Reich are the most influential authors; Behavioral Public Policy leads journals; UK, US, Sweden, and Germany dominate citations. Co-citation reveals four clusters: (1) behavioral research & technology acceptance, (2) machine learning, (3) financial decision-making (prospect theory), (4) AI & consumer behavior. Thematic mapping shows decision-making and public policy as motor themes; environmental economics is emerging. The field lacks interdisciplinary collaboration and macro-level studies. Future research should focus on AI-based field experiments and behavioral macroeconomics. Policymakers and technology designers can use these clusters to prioritize investments in AI nudge systems and behavioral public policy.