用机器学习找冠心病风险因子，帮医生精准筛查。
Utilizing AI Language Models to Identify Prognostic Factors for Coronary Artery Disease: A Study in Mashhad Residents
- 用多种算法分析患者数据，找出关键风险因子。
- CHAID模型准确率达80%，敏感性和特异性最高。
- 结果对临床筛查有指导意义，适合医疗决策者参考。
背景：冠状动脉疾病是全球主要死因，理解其风险因素对病因、流行和治疗至关重要。本研究利用朴素贝叶斯、REP树、J48、CART和CHAID算法评估马什哈德居民冠心病的预后标志物。方法：基于2009年马什哈德研究数据，使用R 3.5.3和WEKA 3.9.4，通过上述算法确定冠心病预后因素。模型性能通过灵敏度、特异性和准确率比较。病例为冠心病患者，每例配三名对照（共940人）。结果：不同算法识别出的风险因子各异。CHAID识别出年龄、心肌梗死史和高血压；CART包含抑郁评分和体力活动；REP树加入教育水平和焦虑评分；朴素贝叶斯包括糖尿病和家族史；J48强调父亲心脏病史和体重下降。CHAID模型准确率最高（0.80）。结论：CART和CHAID模型的关键预后因子包括年龄、心肌梗死史、高血压、抑郁评分、体力活动和体重指数。朴素贝叶斯、REP树和J48识别出更多因素。CHAID在准确率、灵敏度和特异性上均最优。CART模型结构简单，利于医技人员根据需求选择。
原文摘要 · Abstract (English)
Abstract: Background: Understanding cardiovascular artery disease risk factors, the leading global cause of mortality, is crucial for influencing its etiology, prevalence, and treatment. This study aims to evaluate prognostic markers for coronary artery disease in Mashhad using Naive Bayes, REP Tree, J48, CART, and CHAID algorithms. Methods: Using data from the 2009 MASHAD STUDY, prognostic factors for coronary artery disease were determined with Naive Bayes, REP Tree, J48, CART, CHAID, and Random Forest algorithms using R 3.5.3 and WEKA 3.9.4. Model efficiency was compared by sensitivity, specificity, and accuracy. Cases were patients with coronary artery disease; each had three controls (totally 940). Results: Prognostic factors for coronary artery disease in Mashhad residents varied by algorithm. CHAID identified age, myocardial infarction history, and hypertension. CART included depression score and physical activity. REP added education level and anxiety score. NB included diabetes and family history. J48 highlighted father's heart disease and weight loss. CHAID had the highest accuracy (0.80). Conclusion: Key prognostic factors for coronary artery disease in CART and CHAID models include age, myocardial infarction history, hypertension, depression score, physical activity, and BMI. NB, REP Tree, and J48 identified numerous factors. CHAID had the highest accuracy, sensitivity, and specificity. CART offers simpler interpretation, aiding physician and paramedic model selection based on specific. Keywords: RF, Naïve Bayes, REP, J48 algorithms, Coronary Artery Disease (CAD).
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