Abdullah is an undergraduate student completing a Bachelor of Advanced Computing and a Bachelor of Science at the University of Sydney. He majored in Data Science and Medical Science with the aspiration of combining the skills from both disciplines and helping people in the community by enhancing health outcomes. He is currently interning as a Data Scientist at NSW Health. His research interests include using data to aid the detection of diseases for early diagnosis and personalised medicine.
Machine Learning for Detecting and Predicting Adverse Health Events for the Elderly.
The project leverages in-home behavioral data coupled with contextual data related to age group, ethnicity, and other parameters to provide a highly personalized detection and prevention of adverse events that negatively affect the quality of in-home aging. The study consists of analyzing individual and aggregate living patterns and activities of the elderly based on IoT data. The goal is to provide an efficient carer response protocol by developing machine learning algorithms for detecting and predicting adverse health events for the elderly in a non-obstructive way.