Project Abstract

This study investigates the impact of lifestyle choices, such as diet, alcohol consumption, and tobacco use, on COVID-19 mortality rates. It analyses a diverse dataset from around the world and aims to provide timely and practical insights into the current global pandemic. The study specifically focuses on a detailed examination of various factors related to nutrition and epidemiology, using data science to assess how these factors influence COVID-19 outcomes. Employing sophisticated machine learning classification methods, including Logistic Regression, Support Vector Machines, and Artificial Neural Networks, we analyze data to predict mortality risk patterns associated with prevalent health conditions like obesity and diabetes. Preliminary results indicate significant correlations between lifestyle choices and COVID-19 death rates, providing practical knowledge for focused public health actions. This study aims to fill a substantial knowledge gap in the field of global health security, having the capacity to influence policy decisions and individual actions to effectively address current and future health emergencies.

Keywords: Machine Learning, COVID-19 Mortality, Lifestyle Choices

 Conference Details

Session: Presentation Stream 27 at Presentation Slot 4

Location: GH029 at Wednesday 8th 13:30 – 17:00

Markers: Deb Roy, Oliver Kullmann

Course: MSc Data Science, Masters PG

Future Plans: I’m looking for work