Early-Stage Cardiovascular Disease Prediction Using a Sigmoidtropy-Based Decision Tree

Heart disease (HD) is a significant health issue in the world, and its early and proper prediction is essential to minimize mortality and the development of the disease. Cardiovascular disease (CVD) is one of the diseases that need effective and stable predictive models to assist clinical decision-making. This paper gives a Sigmoidtropy-Based Decision Tree (SDT) model of cardiovascular disease prediction, which improves the traditional decision tree by adding a sigmoid-based formulation of entropy. The heart disease data are first grouped by the K-means clustering method in order to enhance the data representation. The suggested SDT model is tested on the Cleveland heart disease dataset of the UCI repository and compared to the traditional classifiers, such as Naive bayes, random forest, and the traditional Decision Tree models. Experimental findings indicate that the SDT has an accuracy of 99.67 which is better than the performance of Random Forest (76.89%), Decision Tree (76.56%), and Naive Bayes (81.84%) with a lower execution time. Despite the promising performance shown by the results, it needs further validation with more datasets and strong evaluation plans to determine the generalizability

Reimagining Culture and Arts Education through the Integration of Artificial Intelligence and the Sixth Industrial Revolution: Demands, Challenges, and Future Implications

This scoping review explores the demands and integration of Artificial Intelligence (AI) and the Sixth Industrial Revolution (IR 6.0) in culture and arts education. It maps existing literature to understand how these technological advancements reshape educational practices, enhance creativity, and prepare learners for a rapidly evolving digital society. Anchored in Technological Determinism and Social Constructivism, the study investigates five key research questions on AI and IR 6.0 demands, integration challenges, alignment strategies, and educational implications. Findings highlight the transformative potential of AI in delivering personalized learning, fostering creativity, preserving cultural heritage, and facilitating immersive experiences through virtual and augmented reality. Likewise, IR 6.0 demands integration of digital tools, blended learning, interdisciplinary approaches, and sustainability. However, challenges such as resistance to change, limited expertise, unequal access to technology, and ethical concerns persist. The review underscores the need for proactive strategies including project-based learning, equitable access, ethical AI use, and continuous teacher training. It concludes that AI and IR 6.0, if harnessed responsibly, can enrich culture and arts education. Gaps in long-term impact studies, ethics, access, and student perceptions are identified, with recommendations for future research to foster inclusive, innovative, and future-ready educational practices

Heart Disease Prediction using an Ensemble Learning Method: A Study at King Abdullah Hospital in Bisha, Saudi Arabia

The detection of diseases is essential to improving healthcare outcomes and saving lives. Thanks to technological advancements in medicine, machine learning has become a valuable tool for predicting future patient health outcomes. Despite the abundance of available patient data, accurately predicting cardiac disease has become increasingly challenging. In response, we developed an innovative ensemble learning approach (ELA) that combines three powerful machine learning (ML) techniques. Our ELA provides reliable predictions of cardiac disease that surpass those of the individual classification algorithms, resulting in higher accuracy. Our research yields a new combination of classification algorithms that significantly increases the prediction accuracy. We tested our model on a regional dataset collected from King Abdullah Hospital in Bisha, Saudi Arabia. We obtained the best results false negatives (FN ) of 8, true positives (TP) of 70, true negatives (TN) of 72, false positives (FP) of 6, accuracy of 0.9113, sensitivity of 0.8839, specificity of 0.95, PPV of 0.9389, NPV of 0.8878, AUC of 0.9569, F1 of 0.9133 Kappa of 0.8220, MCC of 0.8277 with an ELA comprising logistic regression (LR), extra trees (ET) and support vector machine (SVM) with radial basis function (RBF) kernel. With our ELA, medical professionals can detect cardiac disease and provide timely interventions to prevent potentially life-threatening health issues

Predictive Model of Stunting in Children 6-59 Months of Age in Kirundo Health District, Burundi

An analytical cross-sectional study was conducted among a randomly selected sample of 374 households with at least one child aged 6 to 59 months in the Kirundo health district, Burundi. Sociodemographic, socioeconomic, socio-sanitary factors, food insecurity, behavioral, and environmental data were collected using a structured questionnaire. Children's weight was measured using a standard procedure (SECA scale), their height using a standard UNICEF height rod, and their age was obtained from the birth certificate. Anthropometric data were analyzed using Emergency Nutrition Assessment (ENA for Smart) software. Modeling was performed using logistic regression to eliminate confounding factors, and all independent variables with a significance level less than or equal to 20% in the bivariate analysis were included to explore factors associated with stunting in children aged 6 to 59 months. In this study, the prevalence of stunting is estimated at 61.5%. According to multivariate logistic regression, sex (AOR = 2.83; 95% CI:1.40-5.75), age (AOR= 10.40; 95% CI: 1.21-88.30), food insecurity (AOR = 10.47;95% CI: 3.58-30.61), latrine type (AOR = 6.83; 95% CI: 3.12-14.94), diarrhea (AOR = 2.56; 95% CI: 1.19-5.48), water source (AOR = 3.17; 95% CI: 1.54-6.52), media exposure (AOR = 0.24, 95% CI: 0.11-0.51), nutritional knowledge (AOR = 0.11; 95% CI: 0.05-0.25), birth spacing (AOR = 0.39, 95% CI: 0.16-0.93), complete vaccination (AOR = 0.06; 95% CI: 0.02-0.21), father's occupation (AOR = 0.25; 95% CI: 0.09-0.72), and mother's education (AOR = 0.21; 95% CI: 0.07-0.64) were significantly associated with stunting. The predictive model showed an area under the curve (AUC) of 0.95, indicating excellent discrimination ability. The high prevalence of stunting in this study highlights the importance of urgent action to end this problem

Effect of Nutrients on Cognitive Function during Childhood to Adolescence: A Review

Background: Cognitive functioning and development include making decisions, processing information, and responding properly to the environment. People with healthy brains can identify their skills and modify their cognitive, mental, emotional, and behavioral functions to cope as best they can with various life situations. Methods: Studies from the last 15 years included from various search engines like Google Scholar, Pubmed, Science Direct, Scopus Result: The health of the brain is affected by many situations, including illnesses, injuries, mood disorders, substance addiction, and aging-related changes in the brain. There is evidence of numerous changeable lifestyle factors, even though some cannot be changed: Food and exercise, social interaction and mental activity, as well as alcohol and tobacco use, can all help stabilize or enhance deteriorating cognitive performance. Conclusion: Each macronutrient and micronutrient plays a critical role in supporting cognitive function, and their combined effects may be synergistic due to the interrelated nature of their physiological and biochemical actions

Utilization of Social Media Networks for Teaching Effectiveness in Tertiary Institutions of Cross River State, Nigeria: Implications for Learning and Practice in an Environment of Students with Intellectual Disabilities

Aim: This study examines the use of social media networks for teaching effectiveness in public tertiary institutions of Cross River State, Nigeria: Implications for learning and practice in an environment of students with intellectual disabilities. Four study objectives were stated to guide the research. Four research questions were formulated, and one hypothesis statement was made. A literature review was carried out based on the variables under study, as research gaps were also stated. Method: The study utilize7d the descriptive survey research design. The study population comprised 2,800 academic staff of public tertiary institutions of Cross River State. The sampling techniques adopted for this study were the stratified random sampling technique and the accidental random sampling technique. A total sample of 560 respondents was selected from 2,800 academic staff of public tertiary institutions in Cross River State using 20% of the entire population. A validated 25-item four-point modified Likert scale questionnaire was the instrument used for data collection. The face and content validity of the instrument was established by experts in Test and Measurement from the University of Calabar, Calabar-Nigeria. The reliability estimates of 0.89 for the instruments were established using the Cronbach Alpha method. A descriptive analysis of frequency count, percentages, mean, and standard deviation was used to test the research questions. Results: The results obtained from the data analysis revealed that there was a statistically significant joint relationship between the predictor variables (Twitter, Facebook, WhatsApp) and teachers' teaching effectiveness in tertiary institutions in Cross River State, Nigeria. Conclusion: From the findings of this study, one can conclude that there was a statistically significant joint relationship between the predictor variables (Twitter, Facebook, WhatsApp) and teachers teaching effectiveness in tertiary institutions in Cross River State, Nigeria. Key statistical measures, including mean scores, standard deviation, and inferential tests such as Multiple Linear regression, indicate a positive correlation between social media utilization and improved instructional delivery. The findings suggest the need for inclusive digital strategies to maximize learning outcomes, emphasizing the importance of accessible and adaptive teaching approaches. These insights have critical implications for policy formulation, curriculum design, and pedagogical practices in higher education. Recommendation: Based on the result of the study, it was recommended that since the utilization of social sites by teachers improves teaching effectiveness, learning institutions should enact regulations that will govern the proper and positive use of the various types of social media sites among teachers in institutions to promote teachers' teaching effectiveness

The Effect of Interpersonal Communication on Prevention Behavior of Early Hypertension among Student at SMAN 6 and SMAN 19 Bone

Background: Hypertension is a health issue that is not only experienced by adults but can also develop during adolescence. This condition often continues into adulthood, with essential hypertension in adults frequently stemming from habits and risk factors that emerge during adolescence. Centers for Disease Control and Prevention (CDC) 2023 revealed that one in every 25 adolescents aged between 12 to 19 years old is diagnosed with hypertension. Among adolescents diagnosed with hypertension, 10% were found to have a prior history of prehypertension. Objective: This study aims to determine the effect of interpersonal communication on early hypertension prevention behavior among students of SMAN 6 and SMAN 19 Bone. Materials and Methods: The research design used was Quasi Experiment with pretest-posttest control group design. 110 grade 11 students made up the study population. They were split into two groups: the experimental group, which got an interpersonal communication intervention (n=55), and the control group, which received counseling (n=55). This study was carried out at SMAN 6 and SMAN 19 Bone. Simple random sampling was the method of sampling employed in this study, and a questionnaire was utilized as the research tool to gauge students' knowledge, attitudes, and action both before and after they received the intervention, which had been validated and proven to be reliable. Wilcoxon and Mann-Whitney tests were used for both univariate and bivariate data analysis. Results: This study showed significant differences in knowledge, attitudes, and actions in the experimental group regarding hypertension prevention behaviors, with p-values for knowledge (p=0.017), attitude (p=0.000), and action (p=0.002). Conclusion: The interpersonal communication approach applied in the intervention proved to have an influence on hypertension prevention behavior, including knowledge, attitudes, and actions in students of SMAN 6 and SMAN 19 Bone

Boruta Feature Selection and Deep Learning for Alzheimer’s Disease Classification

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder characterized by cognitive decline, memory impairment, and functional deterioration. The early and accurate classification of AD is crucial for timely intervention and management. This study utilizes the Boruta feature selection method to identify the most relevant features for AD classification, selecting the top 15 features based on importance ranking. Three machine learning models—Deep Neural Networks (DNN), Long Short-Term Memory Networks (LSTM), and Support Vector Machines (SVM)—were evaluated using accuracy, precision, recall, and F1-score as performance metrics. The LSTM model demonstrated the highest accuracy (89.30%), outperforming DNN (88.14%) and SVM (84.19%), owing to its capability of capturing temporal dependencies in inpatient data. Results indicate that deep learning models offer superior performance compared to traditional machine learning approaches in AD classification. The study emphasizes the importance of cognitive, lifestyle, and metabolic features in AD diagnosis while acknowledging limitations such as dataset constraints and model interpretability. Future research should improve explainability, incorporate multi-modal data, and leverage real-time monitoring techniques for enhanced AD detection

Editorial: Fostering Inclusive Education and Psychological Well-being for Students with Intellectual Disabilities in Nigeria

Editoria

Convergence Vs Homogeneity: Exploring Hong Kong’s Identity in Transition

This article explores the multiple drivers behind Hong Kong’s identity transition through the lens of the disappearing neon signs. Its cultural and political significances are analyzed through the theoretical frameworks of identity politics, decolonization, and nationalism. The simultaneous forces of decolonization and mainlandization largely accounts for the intricate politicalization of many issues in Hong Kong, including its iconic neon signs, whose connotations has gone through several transitions: from being historical (Western influence), to economic (as a prosperous entrepot and shopping paradise), cultural (unique hybrid of glocalization), technological (becoming outdated in energy efficiency) and even political (fading away after the strengthened regulation in 2010), especially when its early development was a result of bottom-up participation at a grassroots level, while their removal came from a top-down approach through government regulations. The study design incorporates both quantitative and qualitative methods by combining survey results with interviews and policy paper analysis to explore the multiple drivers and the perceived effects on Hong Kong’s identity. This then informs discussions of how to maintain Hong Kong’s position as a space for convergence while developing some new features of in-betweenness