StackDPP:基于堆叠的可解释分类器用于抑郁症预测和在临床医生中发现风险因素
Fahad Ahmed Al-Zahrani1, Lway Faisal Abdulrazak2, Md Mamun Ali3,4
1Computer Engineering Department, Umm Al-Qura University, Mecca 24381, Saudi Arabia.
Bioengineering (Basel, Switzerland)
|July 29, 2023
概括
这项研究引入了一种机器学习模型,用于预测医生抑郁症,识别关键风险因素. 斯塔克DPP模型实现了高精度,帮助心理健康专业人员在治疗决策.
科学领域:
- 医疗信息学 医疗信息学
- 计算精神病学是一种计算精神病学.
- 医疗保健中的机器学习
背景情况:
- 在全球范围内,医生心理健康是一个关键问题.
- 在医生中确定抑郁症风险因素是具有挑战性的.
- 准确的预测模型是需要及时干预的.
研究的目的:
- 开发一种基于机器学习的医生抑郁症预测模型.
- 为了确定与医生抑郁症相关的重大风险因素.
- 评估各种分类算法的性能.
主要方法:
- 收集和预处理的医生健康数据.
- 使用了七个分类算法,包括一个新的堆叠组合分类器 (StackDPP).
- 在10个子数据集上测试模型以优化属性选择.
主要成果:
- 拟议的StackDPP模型在所有数据集中表现出卓越的性能.
- 使用所有属性实现了最高精度 (0.962581).
- 最好的20个属性产生了准确度 (0.96129),与使用所有属性相比.
- 确定了医生抑郁症的重大风险因素.
结论:
- 斯塔克DPP模型有效地预测了医生的抑郁水平.
- 该模型准确地确定了抑郁症的关键风险因素.
- 研究结果支持医生心理健康专业人员加强治疗和治疗规划.
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