基于年龄,脂肪食物消耗和焦虑因素的冠状动脉疾病风险建模,使用惩罚性脊柱非参数逻辑回归
Nur Chamidah1,2, Budi Lestari2,3, Hendri Susilo2,4
1Department of Mathematics, Faculty of Science and Technology, Airlangga University, Surabaya 60115, Indonesia.
MethodsX
|May 8, 2025
概括
一种使用处罚分线非参数逻辑回归的新方法准确地模拟了冠心病 (CHD) 风险. 这种方法识别了年龄,饮食和焦虑等因素之间的非线性关系,改善了疾病预防工作.
科学领域:
- 公共卫生 公共卫生
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 冠心病 (CHD) 是印度尼西亚的主要疾病和死亡原因.
- 现有的模型可能无法完全捕捉到CHD风险因素的复杂相互作用.
- 准确的风险预测对于有效的公共卫生干预至关重要.
研究的目的:
- 开发和验证一种用于建模冠心病 (CHD) 风险的新方法.
- 分析年龄,脂肪食物消费和焦虑对心脏病风险的影响.
- 评估拟议的建模方法的有效性.
主要方法:
- 在Airlangga大学医院进行了横截面调查.
- 采用了处罚分线非参数逻辑回归 (PSNLR) 方法.
- 分析的重点是确定影响因素和心脏病风险之间的关系.
主要成果:
- 该PSNLR模型实现了94.31%的分类准确度.
- 接收器操作特征 (ROC) 曲线的曲线下面面积 (AUC) 为0.96.6.
- 该方法有效地确定了风险因素和心脏病发病率之间的非线性关联.
结论:
- 拟议的PSNLR方法是建模心血管疾病风险的强大工具.
- 这种方法增强了在心血管疾病风险因素中的非线性关系的识别.
- 这些发现通过改善非传染性疾病的预防和治疗来支持可持续发展目标3.
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