使用监督和无监督机器学习识别疾病症状和一般规则
1Department of Industrial Engineering, University of Torbat Heydarieh, Torbat Heydarieh, Iran. f.sogandi@torbath.ac.ir.
Scientific reports
|August 2, 2024
概括
机器学习有效地识别疾病症状和更好的诊断规则. 这种方法有助于医疗保健专业人员和患者及时和经济有效地管理疾病.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 医疗保健中的人工智能
背景情况:
- 早期发现疾病症状对于有效的管理和治疗至关重要.
- 机器学习 (ML) 为医疗保健应用提供了先进的功能.
- 识别症状模式有助于理解和预测疾病的进展.
研究的目的:
- 通过监督和无监督机器学习来识别症状模式和一般规则.
- 通过整合基于规则的ML和分类方法来扩展预测模型.
- 分析在线患者数据,以改善疾病预测和规则发现.
主要方法:
- 将Apriori算法应用于Kaggle患者数据,以发现频繁的症状模式.
- 利用监督和无监督的机器学习技术.
- 对比逐步回归,支向量机,启动森林,增强树木和神经增强方法用于疾病预测.
主要成果:
- 逐步回归通过交叉验证证明了优于其他ML模型的性能.
- 提取了许多重要的决策规则,简化了临床应用.
- 确定了症状和疾病之间的关系,以及不同疾病之间的关系.
结论:
- 该研究成功地发现了使用ML的疾病症状和一般规则.
- 提取的规则可以简化临床工作流程,帮助诊断.
- 拟议的算法提高了预测模型的性能,并支持医疗保健专业人员和患者.
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