基于整体和深度学习方法的智能糖尿病分类和感知框架
Qazi Waqas Khan1, Khalid Iqbal2, Rashid Ahmad2,3
1Department of Computer Engineering, Jeju National University, South Korea, Jeju-si, Jeju, South Korea.
PeerJ. Computer science
|April 25, 2024
概括
这项研究使用机器学习开发了一种早期糖尿病预测系统. 人工神经网络 (ANN) 和随机森林 (RF) 模型在识别糖尿病风险方面取得了超过99%的准确性.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 医疗保健中的机器学习
背景情况:
- 血糖水平升高会造成严重的健康风险,包括失明,脏,脏和心脏疾病.
- 糖尿病导致了全球大量的死亡率,糖尿病患者每年平均死亡率为38%.
研究的目的:
- 开发和评估用于早期糖尿病发病预测的机器学习分类器.
- 为了确定准确的糖尿病风险评估的关键特征.
主要方法:
- 雇佣了奇方位,相互信息和顺序特征选择 (SFS) 来进行特征选择.
- 训练并比较多个分类器:人工神经网络 (ANN),随机森林 (RF),梯度增强 (GB),Tab-Net和支持向量机 (SVM).
- 在PIMA和早期风险糖尿病数据集上验证了系统.
主要成果:
- 在PIMA数据集上,ANN实现了99.35%的准确性.
- 射频模型在早期糖尿病风险数据集上获得了99.36%的准确性.
- 与用于糖尿病预测的基线机器学习算法相比,提出的方法显示出更高的性能.
结论:
- 开发的特征选择和分类方法使得糖尿病的准确早期诊断成为可能.
- 机器学习模型,特别是ANN和RF,在预测糖尿病风险方面表现出很高的有效性.
- 这些发现表明,自动化糖尿病预测系统取得了重大进展.
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