Machine Learning with Applications to Heart Failure Data Analysis and Processing

Saqib Awan

Research output: ThesisDoctoral Thesis

374 Downloads (Pure)


Heart Failure (HF) is a highly prevalent and deadly chronic condition, with high mortality rates and expensive treatments around the world. HF healthcare can be improved through automated Machine Learning (ML) systems, to assist medical experts in diagnosis and decisions. ML systems depend on high quality data. Class imbalance and missing data are major problems of real-world data. My thesis addresses these data issues, with a focus on assisting HF patients of Western Australia. I have developed improved ML models to predict 30-day HF readmissions, deal with class imbalance, and effectively impute missing values in data.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • The University of Western Australia
  • Bennamoun, Mohammed, Supervisor
  • Sohel, Ferdous, Supervisor
  • Dwivedi, Girish, Supervisor
  • Sanfilippo, Frank, Supervisor
Award date14 May 2021
Publication statusUnpublished - 2021


Dive into the research topics of 'Machine Learning with Applications to Heart Failure Data Analysis and Processing'. Together they form a unique fingerprint.

Cite this