机器学习框架用于动脉样硬化心血管疾病风险评估
Parya Esmaeili1,2, Neda Roshanravan3, Saeid Mousavi2
1Liver and Gastrointestinal Diseases Research Center, Tabriz University of Medical Sciences, Tabriz, Iran.
Journal of diabetes and metabolic disorders
|May 31, 2023
概括
机器学习模型准确地确定了关键的动脉样硬化心血管疾病 (ASCVD) 风险因素. 人工神经网络 (ANN) 的表现最好,强调女性的性别,年龄,吸烟和代谢综合征是预防的关键预测因素.
科学领域:
- 心血管疾病研究研究
- 医疗保健中的机器学习
- 生物医学数据科学 生物医学数据科学
背景情况:
- 动脉样性心血管疾病 (ASCVD) 是全球主要的死亡原因.
- 识别个体风险因素对于有效的预防策略至关重要.
研究的目的:
- 使用机器学习 (ML) 方法识别ASCVD的个体风险因素.
- 开发和验证用于预测ASCVD风险的ML模型.
主要方法:
- 对500名ASCVD参与者进行了基于队列的横截面研究.
- 使用了多种ML模型,包括Naive Bayes,SVM,RT,KNN,ANN,GAM和LR.
- 模型性能使用准确度,灵敏度,特异性,PPV,NPV,LR+,LR-和AUC等指标进行评估.
主要成果:
- 模型的准确性很高,从95.7%到98.1%不等.
- 人工神经网络 (ANN) 模型表现出卓越的性能,准确率为98.1%,特异性为99.1%,AUC为99.4%.
- 由ANN确定的关键预测因素包括女性的性别,年龄,吸烟和代谢综合征.
结论:
- ANN成为预测ASCVD风险的最佳模型.
- 鉴定的风险因素 (女性性别,年龄,吸烟,代谢综合征) 可以为有针对性的预防计划提供信息.
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