基于机器学习的结核病 (ML-TB) 健康预测模型:早期结核病健康预测与ML模型用于发展中国家的预防
Priyanka Karmani1, Aftab Ahmed Chandio1, Imtiaz Ali Korejo1
1Institute of Mathematics and Computer Science, University of Sindh, Jamshoro, Sindh, Pakistan.
PeerJ. Computer science
|December 9, 2024
概括
机器学习算法对早期结核病 (TB) 检测有希望. 预测模型实现了高准确度,有助于预防这种全球传染病杀手.
科学领域:
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
- 机器学习 机器学习
背景情况:
- 结核病 (TB) 是全球主要的传染病死亡原因,特别是在发展中国家.
- 早期预防策略对于打击结核病至关重要.
- 一个独特的数据集,包括1200个患者记录,从巴基斯坦辛德省的三个主要医院收集.
研究的目的:
- 开发用于早期预防结核病的机器学习 (ML) 原型.
- 为了确定结核病预测的关键症状,征兆和危险因素.
- 评估不同ML算法在诊断结核病方面的有效性.
主要方法:
- 使用了五个基准ML算法:决策树 (DT),高斯的天真贝叶斯 (GNB),后勤回归分类器 (LRC),自适应增强 (AdaBoost) 和神经网络 (NN).
- 性能评估包括准确性,精度,回忆,F1得分和混矩阵分析.
- 来自巴基斯坦的1200个患者记录的数据集构成了模型开发的基础.
主要成果:
- 所有评估的ML分类器都显示出有效的结核病诊断.
- 准确率在89.04% (GNB) 到93.42% (AdaBoost) 之间.
- AdaBoost获得了93.42%的最高准确率,紧随其后的是神经网络 (92.98%) 和决策树 (92.11%).
结论:
- 机器学习算法是早期发现和诊断结核病的有效工具.
- 开发的原型为结核病预防策略提供了可行的解决方案.
- 这项研究强调了ML在应对结核病等全球卫生挑战方面的潜力.
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