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Mharcelyn M. Kiram
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Journal : natural sciences engineering and technology journal

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Traditional vs. Tech-Driven: A Comparative Analysis of Service Delivery Models in Line Agencies across Urban and Rural Sulu, Philippines Datu Ansaruddin K. Kiram; Mharcelyn M. Kiram
Natural Sciences Engineering and Technology Journal Vol. 5 No. 1 (2025): Natural Sciences Engineering and Technology Journal
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/nasetjournal.v5i1.59

Abstract

This study investigated the impact of technology on public service delivery in Sulu, Philippines, by comparing traditional and tech-driven models in line agencies across urban and rural settings. The research aimed to identify the benefits, challenges, and factors influencing the adoption and effectiveness of technology in enhancing citizen access, satisfaction, and efficiency. A mixed-methods approach was employed, combining quantitative surveys of citizens (n=300) and government employees (n=150) with qualitative interviews of key stakeholders (n=20) in both urban and rural line agencies. Data analysis included descriptive statistics, comparative analysis, and thematic analysis of interview transcripts. Simulated data was generated based on existing literature and reports to supplement primary data collection where access was limited. Tech-driven service delivery models in urban areas led to increased citizen access, reduced processing times, and improved transparency. However, challenges persisted in rural areas due to limited infrastructure, digital literacy gaps, and cultural preferences for traditional approaches. Factors influencing successful technology adoption included leadership commitment, staff training, community engagement, and ongoing technical support. In conclusion, this study highlights the transformative potential of technology in public service delivery in Sulu while emphasizing the need for context-specific strategies to address the unique challenges in rural communities. Recommendations include targeted investments in infrastructure, digital literacy programs, and culturally sensitive technology integration to ensure equitable access and maximize the benefits of tech-driven service delivery across Sulu.
Predictive Modeling in Cardiovascular Disease: An Investigation of Random Forests Mudzramer A. Hayudini; Datu Ansaruddin K. Kiram; Mharcelyn M. Kiram; Abdulkamal H. Abduljalil; Nureeza J. Latorre; Fahra B. Sahibad
Natural Sciences Engineering and Technology Journal Vol. 5 No. 1 (2025): Natural Sciences Engineering and Technology Journal
Publisher : HM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37275/nasetjournal.v5i1.60

Abstract

Cardiovascular diseases (CVDs) are a leading cause of death worldwide. Early detection and intervention are crucial for improving patient outcomes. Machine learning (ML) offers promising tools for CVD prediction, with random forests (RF) emerging as a robust and versatile algorithm. This study investigates the application of RF in predicting blood pressure categories, a crucial indicator of cardiovascular health, using a comprehensive dataset of patient metrics. This study investigated the application of RF in predicting blood pressure categories, a crucial indicator of cardiovascular health. A meticulously curated dataset from Kaggle, comprising 68,205 records and 17 features, was utilized. Key features such as weight, systolic and diastolic blood pressure (ap_hi, ap_lo), cholesterol, glucose, smoking, alcohol consumption, physical activity, and age were selected for predictive modeling. The RF model was trained and tested using a stratified split, and its performance was evaluated using accuracy, precision, recall, F1-score, and confusion matrix. The RF model demonstrated exceptional accuracy in predicting blood pressure categories, achieving an accuracy score of 0.9999. The model also exhibited perfect precision and recall across all categories, indicating its ability to effectively capture complex relationships within the data and make reliable predictions. In conclusion, the findings validate the efficacy of RF as a powerful tool for CVD prediction. Its ability to handle complex interactions and provide accurate predictions underscores its potential to aid healthcare professionals in early diagnosis and personalized intervention strategies. Further research can explore the application of RF in predicting other CVD risk factors and outcomes.
Co-Authors Abdulkamal H. Abduljalil Adzlan P. Hamsaji Datu Ansaruddin K. Kiram Fahra B. Sahibad Jul-Asri A. Hadjibun Merhana Taraji Mudzramer A. Hayudini Nureeza J. Latorre