Survival Modelling of Breast and Brain Cancer Using Statistical Maximum Likelihood and SVM Techniques

Abstract

The research focuses on two main objectives that examine the Burr Type XII distribution through MLE parameter estimation and a comparison between MLE and SVM methods. Survival-related functions such as the survival function and hazard rate and other derived reliability measures are estimated by executing both methods on breast and brain cancer patient real-world data. The input layer of the proposed SVM framework contains distribution parameter specifications that produce output estimates for the reliability function and hazard rate function as well as probability density function, reversed hazard rate function, Mills ratio, and odds function. The research data shows how the hazard function grows after diagnosis then declines toward the end of the study period which reflects the theoretical behaviour patterns of Burr Type XII distributions. The survival analysis demonstrates that theoretical characteristics of the Burr Type XII distribution match experimental results thus validating its usage as cancer survival data model. This SVM method shows itself to be an accurate and stable approach for critical survival parameter prediction.