Machine Learning with Applications to Heart Failure Data Analysis and Processing

Saqib Awan

doi:10.26182/txgf-j027

Machine Learning with Applications to Heart Failure Data Analysis and Processing

Saqib Awan

Research output: Thesis › Doctoral Thesis

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Abstract

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 language	English
Qualification	Doctor of Philosophy
Awarding Institution	The University of Western Australia
Supervisors/Advisors	Bennamoun, Mohammed, Supervisor Sohel, Ferdous, Supervisor Dwivedi, Girish, Supervisor Sanfilippo, Frank, Supervisor
Award date	14 May 2021
DOIs	https://doi.org/10.26182/txgf-j027
Publication status	Unpublished - 2021

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10.26182/txgf-j027

THESIS - DOCTOR OF PHILOSOPHY - AWAN, Saqib Ejaz - 2021
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Cite this

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abstract = "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.",

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AB - 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.

KW - Machine Learning

KW - Artificial Neural Networks

KW - Heart Failure

KW - Missing data

KW - Class imbalance

U2 - 10.26182/txgf-j027

DO - 10.26182/txgf-j027

M3 - Doctoral Thesis

ER -

Machine Learning with Applications to Heart Failure Data Analysis and Processing

Abstract

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