Dr. Mohamad Haider bin Abu Yazid
Dr. Mohamad Haider bin Abu Yazid is a Senior Lecturer at the Faculty of Management, Universiti Teknologi Malaysia (UTM), and a member of the Information and Service Systems Innovation Research Group (ISSIRG). He holds a PhD in Computer Science from UTM, where his doctoral research focused on developing an acute decompensated heart failure mortality prediction model using a hybrid fuzzy flower pollination–trained neural network. His academic training and research background position him at the intersection of artificial intelligence, information systems, and healthcare analytics. Dr. Haider’s expertise spans machine learning, predictive modelling, soft computing, data analytics, and intelligent systems. His work integrates computational intelligence techniques with practical applications, particularly in clinical decision support and risk prediction systems. He has contributed to projects involving non-intrusive inspection systems, quality management, medical predictive modelling, and data-driven decision making. His research portfolio demonstrates a commitment to solving real-world problems using advanced analytic methods and applied artificial intelligence. In the Faculty of Management, Dr. Haider actively supervises students in areas related to predictive analytics, business intelligence, health informatics, and information systems innovation. He is experienced in guiding projects involving model development, optimization, data pipelines, and explainable AI, with a strong emphasis on producing applicable outcomes that support organizational decision-making. His ongoing collaborations with healthcare institutions reflect his interest in translating analytics research into practical tools for operational improvement and clinical insight. With a balance of technical depth and application-driven research, Dr. Haider continues to contribute to emerging areas of analytics, AI adoption, and digital transformation within the healthcare and business domains.
Indexed-Articles (Scopus & Web of Science)
1. Yazid, M. H. A., Talib, M. S., & Satria, M. H. (2019). Heart disease classification framework using fuzzy and flower pollination neural network. International Journal of Advanced Trends in Computer Science and Engineering, 8(1.6), 135-139.
2. Yazid, M. H. B. A., Talib, M. S., & Satria, M. H. (2019, August). Flower pollination neural network for heart disease classification. In IOP Conference Series: Materials Science and Engineering (Vol. 551, No. 1, p. 012072). IOP Publishing.
3. Yazid, M. H. A., Satria, M. H., Talib, S., & Azman, N. (2018, October). Artificial neural network parameter tuning framework for heart disease classification. In 2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) (pp. 674-679). IEEE.
4. Yazid, M. H. A., Satria, M. H., Zahid, M. S. M., Talib, M. S., Haron, H., & Abd Ghazi, A. (2018). Clinical pathway variance prediction using artificial neural network for acute decompensated heart failure clinical pathway. Malaysian Journal of Fundamental and Applied Sciences, 14(1), 116-124.
5. Yazid, M. H. A., Talib, M. S., Satria, M. H., Haron, H., & Abd Ghazi, A. (2020). Mortality prediction for acute decompensated heart failure patient using fuzzy neural network. Malaysian Journal of Fundamental and Applied Sciences, 16(4), 469-474.
Research Interest
Business Analytics, Information Systems, Machine Learning, Artificial Intelligence, Healthcare Informatics
Number of PhD supervisees
Availability of Supervision
Yes
Research Title Available for Supervision
1. Integrating Business Intelligence and Explainable AI to Improve Mortality Risk Stratification in Cardiac Surgery
2. Business Analytics Approach to Identifying Key Drivers of Prolonged Hospitalization Using Explainable ML Techniques
3. Predictive Business Analytics Framework for Early Identification of Post-Operative Complications
4. Explainable Machine Learning for Complication Risk Prediction: Insights for Clinical and Operational Decision-Making
5. Predicting Hospital Bill Using Business Analytics and Machine Learning: Toward Data-Driven Cost Management
6. Cost Classification in Cardiac Surgery Using Predictive Analytics for Strategic Financial Planning
7. Business Analytics Model for 30-Day Readmission Prediction in Cardiac Care
8. Explainable Predictive Analytics for Reducing Readmission Rates in Tertiary Hospitals
