单标签机器学习分类揭示了五种精神障碍疾病的隐藏但相互相关的原因
Hilary I Okagbue1, Ogochukwu A Ijezie2, Paulinus O Ugwoke3,4
1Department of Mathematics, Covenant University, Ota, Nigeria.
Heliyon
|September 7, 2023
概括
这项研究使用机器学习来分析电子健康记录,以诊断尼日利亚的精神障碍疾病 (PDD). 特性选择和新的指标显示了准确检测和分类PDD的有希望的结果.
科学领域:
- 计算精神病学是一种计算精神病学.
- 机器学习在医疗保健中的应用
- 电子病历分析 电子病历分析
背景情况:
- 精神病障碍疾病 (PDD) 显著损害认知,情感和社会功能.
- 由于疾病的复杂性,PDD的准确诊断具有挑战性,需要先进的分析方法.
- 电子健康记录 (EHR) 为理解和诊断心理健康状况提供了丰富的数据来源.
研究的目的:
- 在尼日利亚,应用单标签分类 (SLC) 机器学习方法来挖掘用于PDD诊断的EHR.
- 用ReliefF算法评估特征选择的有效性,并提出新的性能指标.
- 评估使用PDD作为预测因素对其他相关疾病的分类的影响.
主要方法:
- 在尼日利亚EHR数据上使用单标签分类 (SLC) 机器学习模型.
- 采用ReliefF算法进行特征选择,确定五种PDD的重要人口变量:失眠,精神分裂症,注意力缺陷/多动症 (ADHD),血管痴呆症 (VD) 和双极性障碍 (BD).
- 开发了基于RMSE的四个新的性能指标,以评估具有或没有特征选择的模型性能,并使用斯皮尔曼等级相关性量化特征重要性.
主要成果:
- 特性选择和交叉验证对SLC精度的影响最小,新指标接近于零.
- 包括PDD作为预测因素显著提高了分类准确性,精度和回忆力,正如四个新指标所示.
- 差异分析证实了评估指标的分类准确性和精确性的显著差异.
结论:
- 结合特征选择的SLC方法,对于从EHR数据中诊断PDD是有效的.
- 拟议的绩效指标提供了一种可靠的方式来评估PDD研究中的分类模型.
- 利用PDD作为预测器可以提高诊断能力,为心理健康信息学提供了一个有前途的途径.
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