Description
The Machine Learning for Health Sciences (ML4HS) research group is dedicated to advancing the application of machine learning and artificial intelligence (AI) to solve complex challenges in the health sciences. Our interdisciplinary team brings together experts in statistics, data science, chemistry, healthcare and biomedical research to develop innovative algorithms, models, and tools that improve disease diagnosis, drug discovery, public health outcomes and early detection and monitoring of diseases. 

Our research can be used to:

• predict and diagnose diseases such as cancer, obesity, cardiovascular conditions, and infectious diseases using clinical, genomic, and image data (e.g. MRI, CT scans).
• integrate and analyse heterogeneous healthcare data (e.g., electronic health records, wearable device data) to uncover patterns and insights that can lead to improved patient outcomes and personalized treatments. 
• accelerate drug discovery and development using AI driven approaches such as virtual screening and molecular modelling. 

Collaborators
UP Staff and Students acting as Principal Investigators include:
Najmeh Nakhaeirad
Sphiwe Skhosana
Alex Kelbrick

UP Staff Acting as Local Collaborators:
Prof. Vinesh Maharaj (Department of Chemistry)
Prof. Melvin Ambele (Department of Oral and Maxillofacial Pathology, Institute for Cellular and Molecular Medicine)

International collaborators:
Prof Din Chen (Arizona State University, USA) 
Prof Henry Adeola (University of Vanderbilt, USA)

Impact of our research
Our research links to the following UN SDGs:
SDG 3: Good Health and Well-being
SDG 8: Decent work and economic growth