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通过医疗信息学中优化的元启发性建模来进行高级CKD检测
Anas Bilal1,2, Abdulkareem Alzahrani3, Abdullah Almuhaimeed4
1College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Scientific reports
|June 1, 2024
概括
这项研究引入了一个新的元启发模型,用于准确的慢性病 (CKD) 诊断. 该方法通过优化特征选择和数据处理以更好地预测疾病来提高机器学习性能.
科学领域:
- 医疗信息学 医疗信息学
- 机器学习 机器学习
- 计算生物学 计算生物学
背景情况:
- 准确的医疗数据分类对于医疗信息学中的疾病预测至关重要.
- 机器学习和深度学习模型显示出希望,但面临诸如高维度和计算复杂性等挑战.
- 慢性病 (CKD) 诊断的现有方法需要提高效率和准确性.
研究的目的:
- 提出一种新的分类模型,使用元启发式方法来有效诊断慢性病 (CKD).
- 解决医疗数据分类和疾病检测现有技术的局限性.
- 通过优化特征选择和分类,提高CKD预测的准确性和效率.
主要方法:
- 医疗数据的广泛预处理,包括缺失值归算,转换和规范化.
- 应用二进制灰狼优化方法,用于可靠的子集特征选择.
- 使用极端学习机器 (ELM) 以优化的隐藏节点进行CKD分类.
主要成果:
- 拟议的模型在慢性病 (CKD) 诊断方面表现出高准确性.
- 与现有模型相比,元启发特征选择显著提高了预测准确性.
- 优化的极端学习机器 (ELM) 有效地对CKD存在进行了分类.
结论:
- 新的基于元启发的分类模型为慢性病 (CKD) 诊断提供了更准确,更有效的方法.
- 优化功能选择和数据处理是提高医疗信息学机器学习性能的关键.
- 这项研究为改善疾病检测和医疗信息系统做出了有价值的贡献.
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