机器学习模型用于早期检测和准确分类2型糖尿病的应用
Orlando Iparraguirre-Villanueva1, Karina Espinola-Linares2, Rosalynn Ornella Flores Castañeda3
1Facultad de Ingeniería y Arquitectura, Universidad Autónoma del Perú, Lima 15842, Peru.
Diagnostics (Basel, Switzerland)
|July 29, 2023
概括
早期发现糖尿病至关重要. 机器学习模型,特别是K-近邻 (K-NN) 和伯努利天真贝叶斯 (BNB),在准确预测2型糖尿病风险方面表现有前途.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 公共卫生 公共卫生
背景情况:
- 早期发现糖尿病对于预防患者严重并发症至关重要.
- 2型糖尿病对全球健康构成重大挑战,需要改进诊断工具.
研究的目的:
- 使用各种机器学习模型检测和分类2型糖尿病.
- 确定最有效的机器学习模型来预测糖尿病风险.
主要方法:
- 研究了五种机器学习模型:K-近邻 (K-NN),伯努利天真贝叶斯 (BNB),决策树 (DT),逻辑回归 (LR) 和支持向量机器 (SVM).
- 利用Kaggle的Pima印度数据集,包括768名患有糖尿病相关特征的患者记录.
- 基于糖尿病检测预测准确性的评估模型.
主要成果:
- K-最近邻居 (K-NN) 在检测糖尿病方面达到79.6%的最高准确率.
- 伯努利天真贝叶斯 (BNB) 以77.2%的准确度表现出强的表现.
- K-NN和BNB模型的表现优于决策树,后勤回归和支持矢量机器模型.
结论:
- 机器学习模型为早期发现糖尿病提供了一个有希望的方法.
- K-NN和BNB模型是识别2型糖尿病风险人群的有效工具.
- 对ML应用的进一步研究可以提高糖尿病诊断和患者的治疗结果.
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