Predicting breast cancer risk, recurrence and survivability

This thesis focuses on predicting breast cancer at early stages by using machine learning algorithms based on biological datasets. The accuracy of those algorithms has been improved to enable the physicians to enhance the success of treatment, thus saving lives and avoiding several further medical tests

The role of various risk factors in the prevalence of cardiac autonomic neuropathy and associated diseases

The objectives of this thesis were three-fold: The first aim was to investigate the roles of various markers in the prevalence and complications of CAN and associated diseases with emphasis on diabetes mellitus. Specifically, this study investigated the role of heart rate variability (HRV) markers as well as the roles of genetic and family history risk factors. The second aim of this study was to develop mechanisms to predict CAN disease occurrence. The third aim of this current study was to develop a model for predicting diabetes mellitus (DM) and cardiovascular disease (CVD) simultaneously using common risk factors