功能性便秘中直肠出血的营养和生活方式预测因素:一种机器学习方法
Joyeta Ghosh1, Jyoti Taneja2, Ravi Kant3
1Department of Dietetics and Applied Nutrition, Amity Institute of Applied Sciences (AIAS), Amity University - Kolkata Campus, Major Arterial Road, Action Area II, Kadampukur Village, Rajarhat, Newtown, Kolkata, West Bengal 700135, India.
年轻人的直肠出血与低纤维摄入量和较高的BMI有关. 机器学习模型有效预测风险,帮助早期干预和个性化的饮食预防策略.
科学领域:
- 胃肠病学 胃肠病学
- 预防医学 预防医学
- 数据科学数据科学数据科学
背景情况:
- 结直肠出血是年轻人日益关注的问题,通常与慢性便秘和生活方式因素有关.
- 早期识别有风险的个体对于有效的预防和干预战略至关重要.
研究的目的:
- 为了确定年轻成年人直肠出血的饮食和生活方式风险因素.
- 开发和评估用于预测直肠出血风险的机器学习模型.
主要方法:
- 一项涉及875名印度大学生的描述性观察研究.
- 通过关于纤维摄入量,身体活动,便秘和身体质量指数 (BMI) 的问卷收集的数据.
- 通过使用精度和ROC-AUC指标评估了多个机器学习算法.
主要成果:
- 低摄入煮熟的蔬菜/麦片 (<50g/天) 和不充分的全谷物 (>25g/天) 与更高的出血率有显著的关联.
- 与正常BMI相比,超重/肥胖个体的直肠出血发生率更高.
- 肯尼布斯分类器获得了最高的准确性 (98.86%) 和ROC-AUC (0.994),与症状相关的变量显示出更大的预测重要性.
结论:
- 食纤维摄入量和BMI是便秘患者直肠出血发生的重要因素.
- 开发的机器学习模型显示了识别有风险的个体的高潜力,作为预测功能性便秘中直肠出血的工具.
- 这些发现表明个性化的饮食策略和预防性健康措施的有用性,并建议在多种人群中进一步验证.
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