Prediction of the breast cancer mortality rate and its effective factors using genetic algorithm and logistic regression

Abstract

Background: Logistic regression is one of the most common models used to predict and classify binary and multiple state responses in medicine. Genetic algorithms search techniques inspired by biology have recently been used successfully as a predictive model. Objective: The aim of this study was to use the genetic algorithm and logistic regression models in diagnosing and predicting factors affecting breast cancer mortality. Method: In this study, data of 2836 people with breast cancer during the years 2014-2018 was examined; their information was recorded in the cancer registration system of Kerman University of Medical Sciences. Death status was considered a dependent variable, while age, morphology, tumor differentiation (grad), residence status, and place of residence were considered independent variables. Sensitivity, specificity, accuracy, and area under the ROC curve were used to compare the models. Results: the logistic regression model determined factors affecting the breast cancer mortality rate, (with sensitivity (0.62), specificity (0.81), area under the ROC curve (0.74), and accuracy (0.84)), and genetic algorithm model (, with sensitivity (0.19), specificity (0.97), area under the ROC curve (0.58) and accuracy (0.87)). Conclusions: The sensitivity and area under the ROC curve of the logistic regression model were higher than those of the genetic algorithm, but the specificity and accuracy of the genetic algorithm were higher than those of the logistic regression. According to the purpose of the study, two models can be used simultaneously.

Keywords

• : Breast cancer
• cross over
• mutation
• Genetic algorithm
• Logistic regression