ANALYSING PREDICTORS OF HEALTHCARE RESOURCE CONSUMPTION AMONG CANCER PATIENTS: A COMPREHENSIVE REGRESSION MODELING STUDY

Abstract

In this particular study, various risk factors such as socio-economic status, demographics, and clinical factors have been successfully identified for lung and oral cancer. The research indicates that patient age, tumour size, node size and blood sugar levels adversely affect cancer patient survival times, with increased values corresponding to decreased survival times. Furthermore, the study addresses the critical issue of selecting an appropriate survival model and concludes that the Weibull survival model is the most suitable option. This conclusion is drawn based on the lower Akaike Information Criterion (AIC) values obtained compared to other models across all cancer types. The findings suggest that survival times estimated from this model are reliable, facilitating predictions of cancer patient survival times based on available data.