使用机器学习技术建模流行病学数据,以检测胃癌风险因素
Kimia Mohammadnezhad1, Mahmod Reza Sahebi2, Sudabeh Alatab3
1Department of Photogrammetry and Remote Sensing, Faculty of Geodesy and Geomatics Engineering, K. N. Toosi University of Technology, 19967-15433, Tehran, Iran.
Journal of gastrointestinal cancer
|July 10, 2023
概括
机器学习准确地识别胃癌 (GC) 风险因素和病例. 这种方法有助于早期检测,可能减少对侵入性手术的需要.
科学领域:
- 在瘤学瘤学.
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 胃癌 (GC) 是一个重要的全球健康负担,排名为第7最常见的癌症和全球癌症死亡率的主要原因.
- 伊朗的胃恶性瘤发病率高于平均水平,使其成为该国最常见的致命癌症.
- 机器学习为整合健康数据与用于疾病预测和诊断的计算分析提供了强大的工具.
研究的目的:
- 为了模拟来自戈尔斯坦队列研究 (GCS) 的胃癌 (GC) 数据.
- 确定有助于GC发展的关键风险因素.
- 开发一种机器学习模型,使用梯度增强来准确识别GC病例.
主要方法:
- 由于GC和非GC病例之间的差异,数据集使用合成少数群体过量采样技术进行了平衡.
- 在70%的数据上训练了一种梯度增强算法,以识别有影响力的因素.
- 剩下的30%的数据用于评估模型在分类GC病例中的准确性.
主要成果:
- 该研究确定了影响胃癌的六个关键因素:年龄,社会经济地位,茶叶温度,体重指数,性别和教育.
- 这些因素的影响率分别为0.24,0.16,0.13,0.13和0.07.
- 梯度增强模型实现了高精度,在测试组中,72名GC患者中正确分类了70名GC患者.
结论:
- 开发的机器学习模型通过利用显著的风险因素有效检测胃癌 (GC).
- 这种预测方法可能会消除对侵入性诊断程序的需求.
- 模型的可靠性和准确性随着较大数据集的增长而提高,显示出强大的概括能力.
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