使用机器学习区分非典型的精神性厌食症和精神性厌食症
Luis E Sandoval-Araujo1, Claire E Cusack1, Christina Ralph-Nearman1
1Department of Psychological & Brain Sciences, University of Louisville, Louisville, Kentucky, USA.
The International journal of eating disorders
|February 14, 2024
概括
机器学习只有在包括体重指数 (BMI) 时,才能准确区分神经性厌食症 (AN) 和非典型的AN. 没有BMI,分类表现显著下降,质疑区分这些饮食障碍的必要性.
科学领域:
- 饮食障碍的诊断 饮食障碍的诊断
- 机器学习在医疗保健中的应用
- 临床心理学 临床心理学
背景情况:
- 神经性厌食症 (AN) 和非典型的神经性厌食症 (AAN) 经常通过体重指数 (BMI) 进行区分,尽管它们表现出类似的临床特征.
- 之前的研究表明,在BMI标准之外,AN和AAN之间的差异很小.
研究的目的:
- 为了评估机器学习 (ML) 算法在区分AN和AAN的有效性,使用一个全面的特征集.
- 确定BMI是否对基于ML的AN与AAN的准确分类至关重要,或者其他特征是否足够.
主要方法:
- 使用后勤回归,决策树和随机森林ML模型对AN和AAN个体的聚合数据集 (N=448) 进行了分析.
- 在两个不同的数据集上训练模型:一个不包括BMI,另一个包括所有人口统计,饮食障碍和BMI的并发症特征.
主要成果:
- 当BMI作为一个特征被纳入时,ML模型实现了可接受的性能 (平均准确率为74.98%,平均AUC为74.75%).
- 当BMI被排除在模型之外时,分类准确性显著下降 (平均准确率为59.37%,平均AUC为59.98%).
结论:
- 身体质量指数 (BMI) 是机器学习算法的关键特征,可以准确区分精神性厌食症和非典型精神性厌食症.
- 在没有BMI的情况下,将其他人口和临床特征纳入并没有显著提高分类准确性.
- 这些发现表明需要重新考虑AN和AAN之间的诊断差异化,因为BMI似乎是主要的区分因素.
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