使用TSA-LSTM两阶段模型预测癌症发病率和死亡率
1School of Internet Economics and Business, Fujian University of Technology, Fuzhou City, Fujian Province, China.
PloS one
|February 20, 2025
概括
生活方式的选择显著影响癌症风险. 这项研究使用先进的数据预处理和TSA-LSTM模型提高了癌症预测,在预测未来癌症趋势方面达到98.96%的准确性.
科学领域:
- 瘤学和公共卫生
- 数据科学和机器学习
背景情况:
- 癌症是全球主要的死亡原因,吸烟,肥胖和缺乏运动等生活方式因素对发病率和死亡率作出了重大贡献.
- 准确的相关性分析和癌症趋势的预测对于公共卫生指导和资源分配至关重要.
研究的目的:
- 通过分析生活方式相关性,提高癌症发病率和死亡率预测的准确性.
- 开发和验证一个针对未来癌症趋势的优化预测模型.
主要方法:
- 数据预处理涉及使用立方线插曲扩展样本,将年度数据转换为月度数据以改进分析.
- 开发了一个双阶段注意长期短期记忆 (TSA-LSTM) 模型,将输入特征注意力和时间性能注意力结合起来,以提高预测.
- 用因子分析来确定影响癌症发展的关键生活方式因素.
主要成果:
- TSA-LSTM模型在长期癌症趋势预测方面表现出高准确度 (98.96%).
- 分析证实生活方式因素 (如吸烟,肥胖,滥用酒精,缺乏运动) 与各种癌症类型之间存在很强的相关性.
- 视觉测试表明,不良生活方式与肺癌,喉癌,口腔癌,乳腺癌,结肠直肠癌和结肠癌的风险增加之间存在直接联系.
结论:
- 改变生活方式对于预防癌症至关重要.
- TSA-LSTM模型为准确的癌症发病率和死亡率预测提供了一个强大的工具.
- 预计未来几年癌症发病率将大幅增加,这凸显了积极的公共卫生干预措施的必要性.
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