基于MapReduce的大数据框架使用关联的Kruskal多核核分类器用于糖尿病疾病预测
R Ramani1, S Edwin Raja2, D Dhinakaran2
1Department of Artificial Intelligence and Data Science, P.S.R Engineering College, Sivakasi, India.
MethodsX
|March 3, 2025
概括
这项研究引入了一种使用机器学习 (ML) 和大数据的早期疾病预测的新方法. 关联式Kruskal Wallis和MapReduce多核 (AKW-MRPK) 框架显著提高了准确性并减少了计算时间.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 大数据分析大数据分析
- 计算健康 计算健康
背景情况:
- 机器学习 (ML) 算法越来越多地用于复杂的任务,如疾病预测.
- 大数据的增长需要加速计算,以便在医疗保健中有效地应用ML.
- 早期疾病预测需要高效的计算方法来利用ML的潜力.
研究的目的:
- 介绍一种新的方法,AKW-MRPK,用于加速早期疾病预测,使用大数据上的ML.
- 通过优化计算技术,提高疾病预后的准确性和速度.
- 为了证明并行多项式内核在医疗数据分析中的有效性.
主要方法:
- 使用关联克鲁斯卡尔·沃利斯模型进行特征选择,以确定重要的属性.
- 通过MapReduce基于选定的特征对多项式内核向量的并行.
- 实施AKW-MRPK疾病预测框架.
主要成果:
- 在早期疾病预测方面,AKW-MRPK框架达到高达92%的准确性.
- 在25名患者中,计算时间减少到0.875毫秒.
- 与传统方法相比,证明了更高的加速度效率 (1.9 ms使用两个节点).
结论:
- AKW-MRPK方法有效地选择属性并加快计算,以改善疾病预测.
- 将多项式内核并行增强医疗保健大数据分析的准确性和速度.
- 拟议的框架为早期疾病预后提供了重大进展.
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