首页> 外文会议>ISPRS Technical Commission VIII Mid-Term Symposium >MULTINOMIAL LOGISTIC REGRESSION PREDICTED PROBABILITY MAP TO VISUALIZE THE INFLUENCE OF SOCIO-ECONOMIC FACTORS ON BREAST CANCER OCCURRENCE IN SOUTHERN KARNATAKA
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MULTINOMIAL LOGISTIC REGRESSION PREDICTED PROBABILITY MAP TO VISUALIZE THE INFLUENCE OF SOCIO-ECONOMIC FACTORS ON BREAST CANCER OCCURRENCE IN SOUTHERN KARNATAKA

机译:多项式物流回归预测概率图,以可视化社会经济因素对卡纳塔卡南部乳腺癌发生的影响

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Multinomial logistic regression analysis was used to develop statistical model that can predict the probability of breast cancer in Southern Karnataka using the breast cancer occurrence data during 2007-2011. Independent socio-economic variables describing the breast cancer occurrence like age, education, occupation, parity, type of family, health insurance coverage, residential locality and socioeconomic status of each case was obtained. The models were developed as follows: i) Spatial visualization of the Urban- rural distribution of breast cancer cases that were obtained from the Bharat Hospital and Institute of Oncology. ii) Socio-economic risk factors describing the breast cancer occurrences were complied for each case. These data were then analysed using multinomial logistic regression analysis in a SPSS statistical software and relations between the occurrence of breast cancer across the socio-economic status and the influence of other socio-economic variables were evaluated and multinomial logistic regression models were constructed. iii) the model that best predicted the occurrence of breast cancer were identified. This multivariate logistic regression model has been entered into a geographic information system and maps showing the predicted probability of breast cancer occurrence in Southern Karnataka was created. This study demonstrates that Multinomial logistic regression is a valuable tool for developing models that predict the probability of breast cancer Occurrence in Southern Karnataka.
机译:多项式逻辑回归分析用于开发统计模型,可以在2007 - 2011年期间使用乳腺癌发生数据来预测南卡纳塔卡群岛乳腺癌的概率。获得了描述乳腺癌发生的独立社会经济变量,如年龄,教育,职业,平价,家庭,健康保险覆盖范围,住宅区,每种案件的社会经济和社会经济地位。该模型开发如下:i)从Bharat医院和肿瘤研究所获得的乳腺癌病例的城乡分布的空间可视化。 ii)每种情况都符合描述乳腺癌发生的社会经济风险因素。然后使用多项逻辑回归分析在SPSS统计软件中分析这些数据,并在社会经济地位发生乳腺癌发生之间的关系,并评估了其他社会经济变量的影响,构建了多项逻辑回归模型。 iii)确定最佳预测乳腺癌发生的模型。该多变量逻辑回归模型已进入地理信息系统,并创建了南卡纳塔卡南部乳腺癌发生概率的地图。本研究表明,多项式逻辑回归是开发南部卡纳塔克南部乳腺癌发生概率的有价值的工具。

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