利用二元逻辑回归模型诊断致癌因素的影响:巴格达肿瘤医院患者样本的应用研究

A. Heydari, Sahera Hussein Zain Al-Thalabi
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引用次数: 0

摘要

由于这一现象的数据是二元的,意味着事件的存在或不存在,因此对二元响应变量应用二元逻辑回归模型,其中因变量在事件发生时等于1,在事件不存在时等于0。本研究旨在利用该模型获得影响癌性肿瘤的最重要因素。该研究包括来自卫生部和肿瘤教学医院统计程序(SPSS)的癌性肿瘤数据。结论表明,逻辑回归模型适合检验数据,不存在多重线性问题。以及影响癌症疾病的六大因素(吸烟、慢性病、家族史、体重、身高和婚姻状况),但性别对癌症疾病没有显著影响,根据样本绘制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Using a binary logistic regression model to diagnose the effect of factors causing cancer : An applied study on a sample of patients at the Oncology Hospital in Baghdad
Abstract Since the data about this phenomenon is binary, meaning the presence or absence of an event, so it applied the binary logistic regression model for the binary response variable in which the dependent variable is either equal to one for the occurrence of the event or zero for the absence of the event. The research aims to use this model to obtain the most important factors affecting cancerous tumors. The study included cancerous tumor data from the Ministry of Health and the Tumor Teaching Hospital, The statistical program (SPSS). The conclusions showed that the logistic regression model is suitable for testing the data and does not suffer from the problem of multilinearity. As well as six factors affecting cancer diseases (Smoking, chronic diseases, family history, weight, height, and marital status), but sex does not have a significant effect on cancerous diseases, according to the sample drawn.
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