遗传算法和逻辑回归预测癌症死亡率及其影响因素

Q4 Medicine
Mahdieh Mirzaie, Y. Jahani, A. Bahrampour
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引用次数: 0

摘要

引言:逻辑回归是医学中用于预测和分类二元和多状态反应的最常见模型之一。受生物学启发的遗传算法搜索技术最近被成功地用作预测模型。本研究的目的是使用遗传算法和逻辑回归模型来诊断和预测影响癌症死亡率的因素。方法:对2014-2018年间2836例癌症患者的数据进行分析。信息在克尔曼医学科学大学癌症注册系统中注册。死亡状态被视为因变量,而年龄、形态、肿瘤分化(grad)、居住状态和居住地点被视为自变量。敏感性、特异性、准确性和受试者操作特征曲线下面积用于比较模型。结果:逻辑回归模型确定了影响乳腺癌症死亡率的因素(敏感性(0.60)、特异性(0.80)、ROC曲线下面积(0.70)和准确度(0.77)),遗传算法模型(敏感性(0.21)、特异性(0.96)和ROC曲线上面积(0.58)和准确率(0.87))也确定了这些因素。结论:logistic回归模型的敏感性和ROC曲线下面积均高于遗传算法,但遗传算法的特异性和准确性高于logistic回归。根据研究目的,可以同时使用两个模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of the breast cancer mortality rate and its effective factors using genetic algorithm and logistic regression
Introduction: 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. The aim of present study was to use the genetic algorithm and logistic regression models in diagnosing and predicting factors affecting breast cancer mortality. Methods: Data of 2836 people with breast cancer during the years 2014-2018 were examined. Information was registered in the cancer registration system of Kerman University of Medical Sciences. Death status was considered as the dependent variable, while age, morphology, tumor differentiation (grad), residence status, and place of residence were considered as independent variables. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic (ROC) curve were used to compare the models. Results: The logistic regression model determined factors affecting the breast cancer mortality rate, (with sensitivity (0.60), specificity (0.80), area under the ROC curve (0.70), and accuracy (0.77)), and also genetic algorithm model (with sensitivity (0.21), specificity (0.96), area under the ROC curve (0.58) and accuracy (0.87)) did so. Conclusion: 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.
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来源期刊
CiteScore
0.80
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0.00%
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审稿时长
12 weeks
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