使用数据挖掘和机器学习算法预测糖尿病:一个横截面研究
Hassan Shojaee-Mend1, Farnia Velayati2, Batool Tayefi3
1Infectious Diseases Research Center, Gonabad University of Medical Sciences, Gonabad, Iran.
Healthcare informatics research
|February 15, 2024
概括
机器学习模型有效地利用年龄,BMI和血压等关键因素预测糖尿病风险. CatBoost 模型表现最好,有助于糖尿病管理和预防策略.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 糖尿病研究 糖尿病研究
背景情况:
- 糖尿病的早期诊断和治疗对于改善患者的治疗结果和生活质量至关重要.
- 预测模型提供了一种有希望的方法来识别患糖尿病风险的个体.
- 机器学习和数据挖掘技术可以提高糖尿病风险预测的准确性.
研究的目的:
- 开发和评估用于预测禁食血糖状况的机器学习模型.
- 通过数据挖掘技术,确定与糖尿病相关的最重要的风险因素.
- 通过准确的风险评估,支持糖尿病管理和预防策略的规划.
主要方法:
- 在伊朗德黑兰的一项糖尿病查计划中对3376名成年人的横截面研究.
- 使用随机抽样和合成少数过量抽样技术 (SMOTE) 进行数据集平衡.
- 通过Shapley值和噪声分析进行特征选择;使用精度,灵敏度,特异性,F1得分和AUC.评估五个ML算法 (CatBoost,随机森林,XGBoost,后勤回归,ANN).
主要成果:
- 年龄,腰比,体重指数和静脉血压被确定为禁食血糖状况的关键预测因素.
- 所有模型都表现出类似的预测能力,CatBoost模型实现了0.737.7的最高曲线下面积 (AUC).
- 渐变增强决策树模型有效地识别了重要的糖尿病风险因素.
结论:
- 机器学习,特别是像CatBoost这样的梯度增强决策树,可以准确地预测糖尿病风险.
- 关键的可修改和不可修改的风险因素包括年龄,腰部和部的比例,体重指数和缩血压.
- 开发的模型可以作为一种有价值的工具,用于糖尿病管理和预防的公共卫生倡议.
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