Iqbal Madakkatel

Dr Iqbal Madakkatel

Research Associate, Machine Learning

School of Public Health

College of Health

Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.


Dr. Iqbal Madakkatel is a Research Associate at the Nutritional and Genetic Epidemiology Research Group, specializing in the application of Machine Learning in Epidemiology and Public Health. His work is at the intersection of data science and health research, leveraging advanced computational techniques to address pressing health challenges. His research focuses on feature selection and risk factor discovery, aiming to uncover critical insights that can inform public health interventions. In addition to applying advanced machine learning algorithms to identify key determinants of health outcomes, he also has a strong background in statistical modelling, which allows him to interpret complex datasets and draw meaningful conclusions. He is passionate about both developing new methodologies and applying existing ones to extract valuable information from health data. His work contributes to the broader understanding of disease patterns and health outcomes, ultimately aiming to improve population health. He is committed to advancing the field of health informatics through rigorous research and innovative approaches.
Having completed his Diploma and bachelor’s degree in computer engineering, he pursued further education and received his M.Sc. degree in information technology, with a focus on informatics. In 2021, he obtained a PhD in Data Science with a focus on Epidemiology/Public Health.
Before his foray into doctoral studies, he held multiple roles within the banking industry over the span of a decade. He possesses an array of professional certifications in project management, quality, and enterprise architecture, reflecting his commitment to professional growth and expertise in various aspects of the industry. He holds a Chartered Engineer membership at the Institution of Engineers (India). His extensive work experience and academic pursuits underscore his passion for continual learning, innovation, and problem-solving in the rapidly progressing landscape of big data analytics, intricate computational modelling, and the rising domain of artificial intelligence.

Year Citation
2026 Geleta, L. A., Doyle, C., Garton, F. C., Fowler, M., Carr, J. M., Akkari, P. A., . . . Benyamin, B. (2026). The roles of human endogenous retrovirus in neurodegenerative diseases: A systematic review. Brain Behavior and Immunity, 132, 13 pages.
DOI
2025 Mulugeta, A., Stacey, D., Lumsden, A. L., Madakkatel, I., Lee, S. H., Mäenpää, J., . . . Hyppönen, E. (2025). Protein markers of ovarian cancer and its subtypes: insights from proteome-wide Mendelian randomisation analysis. British Journal Of Cancer, 133(8), 1208-1217.
DOI
2025 Hyppönen, E., & Madakkatel, I. (2025). Response to Oka and Takefuji's correspondence on the article "Large-scale analysis to identify risk factors for ovarian cancer" by Madakkatel et al.. International journal of gynecological cancer : official journal of the International Gynecological Cancer Society, 35(9), 102005.
DOI
2025 Yeshaw, Y., Madakkatel, I., Mulugeta, A., Lumsden, A., & Hypponen, E. (2025). Machine learning to discover factors predicting volume of white matter hyperintensities: insights from the UK Biobank. Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring, 17(1, article no. e70090), 1-12.
DOI
2025 Hypponen, E., & Madakkatel, I. (2025). Response to Oka and Takefuji's correspondence on the article "Large-scale analysis to identify risk factors for ovarian cancer" by Madakkatel et al. International Journal Of Gynecological Cancer, 35(9, article no. 102000), 1.
DOI
2024 Madakkatel, I., & Hyppönen, E. (2024). LLpowershap: logistic loss-based automated Shapley values feature selection method. BMC Medical Research Methodology, 24(1, article no. 247), 1-14.
DOI Scopus1 WoS2
2024 Yeshaw, Y., Madakkatel, I., Mulugeta, A., Lumsden, A., & Hyppönen, E. (2024). Uncovering predictors of low hippocampal volume: evidence from a large-scale machine-learning-based study in the UK biobank. Neuroepidemiology, 58(5), 369-382.
DOI
2024 Mulugeta, A., Lumsden, A., Madakkatel, I., Stacey, D., Lee, S. H., Mäenpää, J., . . . Hypponen, E. (2024). Phenome-wide association study of ovarian cancer identifies common comorbidities and reveals shared genetics with complex diseases and biomarkers. Cancer Medicine, 13(4, article no. e7051), 1-13.
DOI Scopus2 Europe PMC3
2024 Madakkatel, I., Lumsden, A. L., Mulugeta, A., Mäenpää, J., Oehler, M. K., & Hyppönen, E. (2024). Large-scale analysis to identify risk factors for ovarian cancer. International Journal of Gynecological Cancer, 35(8, article no. 101844), 005424-1-005424-9.
DOI Scopus6 WoS6 Europe PMC5
2024 Yalew, M., Mulugeta, A., Lumsden, A. L., Madakkatel, I., Lee, S. H., Oehler, M. K., . . . Hyppönen, E. (2024). Circulating Phylloquinone and the Risk of Four Female-Specific Cancers: A Mendelian Randomization Study. Nutrients, 16(21), nu16213680-1-nu16213680-9.
DOI
2023 Madakkatel, I., Lumsden, A. L., Mulugeta, A., Olver, I., & Hyppönen, E. (2023). Hypothesis-free discovery of novel cancer predictors using machine learning. European Journal of Clinical Investigation, 53(10), 1-13.
DOI Scopus4 WoS4 Europe PMC4
2022 Madakkatel, I., Chiera, B., & McDonnell, M. (2022). Can personal budget management services improve debt repayments? A study using budget data. Financial Planning Research Journal, 8(1), 10-25.
DOI
2021 Madakkatel, I., Zhou, A., McDonnell, M. D., & Hyppönen, E. (2021). Combining machine learning and conventional statistical approaches for risk factor discovery in a large cohort study. Scientific Reports, 11(1, article no. 22997), 11 pages.
DOI Scopus46 WoS42 Europe PMC38
2010 Rahwan, I., Madakkatel, M. I., Bonnefon, J. F., Awan, R. N., & Abdallah, S. (2010). Behavioral experiments for assessing the abstract argumentation semantics of reinstatement. Cognitive Science, 34(8), 1483-1502.
DOI Europe PMC2

Year Citation
2021 Madakkatel, I., King, C., Zhou, A., Mulugeta, A., Lumsden, A., McDonnell, M., & Hyppönen, E. (2021). Identifying risk factors for COVID-19 severity and mortality in the UK Biobank.
DOI

Date Role Research Topic Program Degree Type Student Load Student Name
2023 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Leta Geleta
2023 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Endeshaw Chekol Abebe
2022 Co-Supervisor - Doctor of Philosophy Doctorate Full Time Mr Yigizie Yeshaw Mihiretie
  • Position: Research Associate, Machine Learning
  • Email: iqbal.madakkatel@adelaide.edu.au
  • Alternative Contact: Tel:0883021936 Location: SAHMRI, Level 8, Southside, Desk Number:90

Connect With Me

External Profiles

Other Links