An Exploratory Data Analysis of Breast Cancer Features in South of Libya

اسمه اعجال, منصور الصغير
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引用次数: 1

Abstract

Exploratory data analysis is a data visualization approach used to extract knowledge from raw data. This approach can be applied to medical data to improve healthcare providers services.  In recent years, breast cancer has become more common in women and requires effective procedures to detect it in the early stage. In this context, breast cancer patients' data were collected from the Sebha oncology center through their routine blood tests. The exploratory data analysis technique is used in this study to better analyze the patients' markers. The analysis aims to discover prominent bio and tumor markers that can assist in determining whether a tumor is benign or malignant. Several statistical and visualizations methods are used. The results show that the most effective markers that may be used as cancer predictors are: Cancer Antigen-15.3, Carcinoma Embryonic Antigen, White Blood Cells, Blood platelets, and Albumin. These findings are consistent with the findings of Sebha oncology center specialists. which may eventually aid in their cancer diagnosis.
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利比亚南部乳腺癌特征的探索性数据分析
探索性数据分析是一种用于从原始数据中提取知识的数据可视化方法。这种方法可以应用于医疗数据,以改善医疗保健提供者的服务。近年来,乳腺癌在女性中变得越来越普遍,需要有效的程序在早期发现它。在这种情况下,通过常规血液检查从Sebha肿瘤中心收集乳腺癌患者的数据。本研究采用探索性数据分析技术,更好地分析患者的标志物。分析的目的是发现突出的生物和肿瘤标志物,可以帮助确定肿瘤是良性还是恶性。使用了几种统计和可视化方法。结果表明,最有效的癌症预测指标是:癌抗原15.3、癌胚抗原、白细胞、血小板和白蛋白。这些发现与Sebha肿瘤中心专家的发现一致。这可能最终有助于他们的癌症诊断。
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