Location
Harare, Zimbabwe and Virtual
Start Date
12-9-2024 4:55 PM
End Date
12-9-2024 5:20 PM
Description
This study investigates the impact of viral infections on adults with chronic illnesses, focusing on the development of a Random Forest classifier model. The research aims to predict outcomes among individuals with conditions like diabetes, cancer, and tuberculosis, analyzing severity, age groups, and travel patterns. The study aims to assist healthcare professionals in resource allocation and patient prioritization based on disease severity. It reviews literature on viral infection risks for chronic illness patients and explores machine learning applications in infectious disease management. Methodologically, the study adopts a structured approach similar to the Cross-Industry Standard Process for Data Mining (CRISP -DM) model, integrating correlational and diagnostic research methods. Results indicate that males show higher susceptibility to severe outcomes, with varying infection rates across severity categories. Travel analysis reveals significant virus spread among travellers compared to non-travellers. Older patients exhibit distinct infection patterns. The Random Forest classifier effectively predicts infection outcomes, offering insights for improving healthcare decision-making and response strategies in managing viral infections among adults with chronic conditions.
Included in
A Random Forest Classifier Model for Predicting the Impact of Viral Infections on Adults with Chronic Conditions
Harare, Zimbabwe and Virtual
This study investigates the impact of viral infections on adults with chronic illnesses, focusing on the development of a Random Forest classifier model. The research aims to predict outcomes among individuals with conditions like diabetes, cancer, and tuberculosis, analyzing severity, age groups, and travel patterns. The study aims to assist healthcare professionals in resource allocation and patient prioritization based on disease severity. It reviews literature on viral infection risks for chronic illness patients and explores machine learning applications in infectious disease management. Methodologically, the study adopts a structured approach similar to the Cross-Industry Standard Process for Data Mining (CRISP -DM) model, integrating correlational and diagnostic research methods. Results indicate that males show higher susceptibility to severe outcomes, with varying infection rates across severity categories. Travel analysis reveals significant virus spread among travellers compared to non-travellers. Older patients exhibit distinct infection patterns. The Random Forest classifier effectively predicts infection outcomes, offering insights for improving healthcare decision-making and response strategies in managing viral infections among adults with chronic conditions.
