从医疗保健的角度使用模糊的 AHP TOPSIS 来选择数据分析技术
Abdullah Alharbi1, Wael Alosaimi1, Hashem Alyami2
1Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia.
BMC medical informatics and decision making
|September 2, 2024
概括
医疗保健中的数据分析对于疾病预测和医院成本效益至关重要. 这项研究确定数据清理是最有影响力的因素,回归分析是排名第一的方法.
科学领域:
- 医疗保健数据分析数据分析
- 机器学习在医学中的应用
- 决策分析框架 决策分析框架
背景情况:
- 医疗保健行业面临着管理来自不同来源的庞大,异构数据的挑战.
- 有效的数据分析 (DA) 和机器学习对于医疗保健决策,预测和分析至关重要.
- 选择最佳的DA方法对于研究人员,科学家和行业专业人士来说至关重要.
研究的目的:
- 进行决策分析,比较数据分析因素和医疗保健中的替代方案.
- 评估DA的好处,并为医疗数据管理提供分析框架.
- 确定对医疗保健行业最有影响力的DA因素和最适合的DA方法.
主要方法:
- 利用模糊的分析层次过程 (模糊的AHP) 来确定各种DA因子的权重.
- 通过与理想解决方案相似的顺序偏好使用模糊技术 (模糊TOPSIS) 来对DA替代品进行排名.
- 评估了DA中的挑战,并提出了在医疗保健背景下解决这些问题的方法.
主要成果:
- "清洁"因素被认为是数据分析中最有影响力的因素.
- 根据模糊的AHP",更新"被发现是最不优先考虑的因素.
- 在DA替代方案中,回归分析获得了最高的排名,而诊断分析排名最低.
结论:
- 决策分析对于数据科学家和医疗提供者来说至关重要,以准确预测疾病.
- 该研究强调了与医院有效数据分析相关的成本效益.
- 开发的框架有助于选择适当的数据分析策略,以改善医疗保健结果.
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