Assist. Prof. Dr. Basil Duwa | Machine learning | Best Researcher Award
Operational Center in Healthcare at Near East University, Turkey
Dr. Basil B. Duwa is a results-oriented biomedical data scientist and engineer with expertise in clinical bioinformatics, machine learning for disease prediction, and medical device innovation. With over five years of research and practical experience in healthcare data science, Dr. Duwa has made notable contributions to parasitology-focused AI, wearable sensor analysis, and multi-criteria decision-making in healthcare. He currently serves as an Assistant Professor and Postdoctoral Fellow at the Operational Research Center in Healthcare, Near East University, where he integrates AI and biomedical engineering for real-world medical applications.
Professional Profile:
Education Background
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Assistant Professor & Postdoctoral Fellow
Near East University, Cyprus (2024–Present)-
Lead AI research in healthcare, predictive modeling, and telemedicine systems.
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Co-authored a book on medical device applications published by Elsevier.
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Clinical Informatics Researcher
Operational Research Center in Healthcare (2022–2024)-
Developed AI models for disease prediction including malaria and COVID-19.
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Integrated MCDM methods into healthcare analytics.
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Research Assistant – Biomedical Data Science
Near East University (2020–2022)-
Focused on predictive models and decision systems for biomedical challenges.
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Monitoring & Evaluation Data Analyst
Plan International & Save the Children (2012–2018)-
Evaluated child health and education data; developed analytical dashboards.
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Research Focus
Dr. Duwa’s interdisciplinary research combines machine learning, bioinformatics, data visualization, and medical device design. His key interests include:
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AI-driven disease prediction and diagnostics
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Wearable sensor data analytics
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Explainable AI in biomedical decision-making
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Multi-criteria decision analysis (MCDM) in healthcare
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Federated learning and clinical applications of AI
Author Metrics:
Awards and Honors:
Publication Top Notes
1. Second-Order Based Ensemble Machine Learning Technique for Modelling River Water Biological Oxygen Demand (BOD): Insights into Improved Learning
Authors: A.G. Usman, M. Almousa, H. Daud, B.B. Duwa, A.A. Suleiman, A.I. Ishaq, …
Journal: Journal of Radiation Research and Applied Sciences
Volume: 18(2)
Article: 101439
Year: 2025
Summary: Developed a second-order ensemble machine learning framework to model and predict BOD levels in rivers, improving environmental monitoring accuracy.
🧠 Focus Area: Environmental ML Modeling / Ensemble Learning
2. Enhanced Drug Classification for Cancers of the Liver with Multi-Criteria Decision-Making Method – PROMETHEE
Authors: B.B. Duwa, N. Usanase, B. Uzun
Journal: Global Journal of Sciences
Volume: 2(1), pp. 24–36
Year: 2025
Summary: Applied PROMETHEE (MCDM) for liver cancer drug classification, improving clinical decision-making through structured and explainable evaluation.
💊 Focus Area: Drug Classification / MCDM / Oncology
3. Improving Telemedicine with Digital Twin-Driven Machine Learning: A Novel Framework
Authors: I. Goni, B. Bali, B.M. Ahmad, B.B. Duwa, C. Iwendi
Journal: Global Journal of Sciences
Volume: 1(2), pp. 58–70
Year: 2025
Summary: Introduces a digital twin-powered machine learning architecture to enhance predictive diagnostics in telemedicine systems.
🌐 Focus Area: Telemedicine / Digital Twins / AI in Healthcare
4. Reply to Graña et al. Comment on “Uzun Ozsahin et al. COVID-19 Prediction Using Black-Box Based Pearson Correlation Approach”
Authors: D. Uzun Ozsahin, E. Precious Onakpojeruo, B. Bartholomew Duwa, …
Journal: Diagnostics
Volume: 14(22), Article: 2529
Year: 2024
Summary: A formal response clarifying methodological insights and addressing critiques on a previously published AI model for COVID-19 prediction.
🧬 Focus Area: Model Interpretability / COVID-19 Forecasting
5. Ensemble Predictive Modeling for Dementia Diagnosis
Authors: B.B. Duwa, E.P. Onakpojeruo, B. Uzun, A.J. Hussain, I. Ozsahin, L.R. David, …
Conference: 17th International Conference on Development in eSystem Engineering (DeSE)
Year: 2024
Summary: Demonstrates the power of ensemble ML techniques in diagnosing dementia, integrating multiple model architectures for increased diagnostic precision.
🧠 Focus Area: Medical AI / Cognitive Disorders / Ensemble Learning