对经典和机器学习方法进行比较分析,用于预测结核病/艾滋病毒共感染
André Abade1, Lucas Faria Porto2, Alessandro Rolim Scholze3
1Federal Institute of Education, Science and Technology of Mato Grosso, Department of Computer Science, Campus Barra do Garças, Barra do Garças, Mato Grosso, Brazil. andre.abade@ifmt.edu.br.
Scientific reports
|August 16, 2024
概括
预测结核病/艾滋病毒共感染病例至关重要. 深度学习模型,如双向LSTM和CNN-LSTM,比公共卫生规划的传统方法显著提高了预测准确性.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 计算生物学 计算生物学
背景情况:
- 结核病/艾滋病毒联合感染是一个重大的全球卫生挑战.
- 准确的预测对于资源分配和干预策略至关重要.
- 预测结核病/艾滋病毒趋势需要先进的分析方法.
研究的目的:
- 为了比较传统的统计和机器学习模型对结核病/艾滋病毒共感染的预测性能.
- 评估各种时间序列预测模型,包括深度学习架构.
- 确定最有效的模型来准确预测结核病/艾滋病毒趋势.
主要方法:
- 使用指数级光滑和ARIMA作为基线的时间序列分析.
- 机器学习模型的应用:SVR,XGBoost,LSTM,CNN,GRU,CNN-GRU,CNN-LSTM. 这些都是机器学习模型的应用.
- 使用MSE,MAE,sMAPE和Diebold-Mariano测试进行性能评估.
主要成果:
- 深度学习模型,特别是双向LSTM和CNN-LSTM,表现出卓越的性能.
- 这些先进的模型显著超过了传统的统计方法.
- 实现了结核病/艾滋病毒共感染趋势的准确预测.
结论:
- 深度学习模型对于模拟复杂的结核病/艾滋病毒共感染动态非常有效.
- 双向LSTM和CNN-LSTM为公共卫生中的时间序列预测提供了更高的准确性.
- 这些发现支持采用深度学习来改善结核病/艾滋病毒监测和规划.
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