机器学习方法研究印度慢性疾病的社会决定因素:比较分析
Aakanksha Agarwala1, Barun Barua2, Genevieve Chyrmang2
1Senior Research Fellow, Department of Statistics, Gauhati University, Guwahati, Assam, India.
机器学习模型可以使用社会决定因素来预测非传染性疾病 (NCD). 随机森林模型实现了87.9%的准确性,为社会流行病学研究提供了一个有前途的工具.
科学领域:
- 社会流行病学社会流行病学
- 医疗信息学 医疗信息学
- 机器学习在公共卫生中的应用.
背景情况:
- 非传染性疾病 (NCD) 构成了全球重大健康挑战.
- 识别可修改的风险因素,包括社会人口统计学,生活方式和行为方面,对于预防NCD至关重要.
- 了解社会决定因素对于应对慢性疾病流行病至关重要.
研究的目的:
- 系统地比较各种机器学习 (ML) 分类器.
- 确定最佳的ML模型来分析非传染性疾病的社会决定因素.
- 利用ML来理解基于社会因素的疾病患病率.
主要方法:
- 利用印度长度衰老研究的数据.
- 对比了25个不同的机器学习算法的性能.
- 使用社会人口统计学,生活方式和行为风险因素预测NCD患病率.
主要成果:
- 随机森林模型表现出最高的性能,准确率为87.9%.
- 使用网格搜索和5倍交叉验证的超参数调整优化了模型.
- 选择的模型准确地预测了新病例中的NCD流行率.
结论:
- 了解社会决定因素对于应对慢性疾病至关重要.
- 机器学习显示出使用临床和社会参数分析疾病的潜力.
- 这项研究鼓励在社会流行病学研究中应用ML.
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