一个有效的基于关联的数据建模框架,用于使用机器和深度学习技术自动预测糖尿病
Kiran Kumar Patro1, Jaya Prakash Allam2, Umamaheswararao Sanapala1
1Department of ECE, Aditya Institute of Technology and Management, Tekkali, AP, 532201, India.
BMC bioinformatics
|October 2, 2023
概括
早期发现糖尿病至关重要. 这项研究引入了一种新的数据建模框架,使用特征相关性,提高机器学习准确度,可靠地预测糖尿病,特别是有限的生物医学数据.
科学领域:
- 生物医学数据科学是生物医学数据科学.
- 机器学习在医疗保健中的应用
- 糖尿病预测研究研究
背景情况:
- 全球糖尿病风险不断上升,需要及早检测.
- 手动预测糖尿病是具有挑战性的,容易出现错误.
- 生物医学数据稀缺和噪音阻碍了有效的深度学习模型培训.
研究的目的:
- 为有效预测糖尿病提供一个新的数据建模框架.
- 为应对生物医学数据集数据稀缺和噪声的挑战.
- 提高自动化糖尿病检测的准确性和可靠性.
主要方法:
- 开发了一个基于特征相关性指标的数据建模框架.
- 将框架应用于皮马印第安人医学糖尿病 (PIMA) 数据集.
- 利用机器学习和深度卷积神经网络模型进行预测.
主要成果:
- 拟议的数据建模方法平均提高了9%的机器学习模型准确性.
- 深层卷积神经网络在糖尿病预测方面实现了96.13%的高精度.
- 证明有效处理有限和杂的生物医学数据.
结论:
- 这种新的框架增强了早期和可靠的糖尿病预测.
- 基于特征相关性的建模有效地克服了生物医学数据的局限性.
- 该方法为改进自动诊断工具提供了一个有希望的策略.
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