Qutrio Baloch Zulfiqar, Raza Syed Ali, Pathak Rahul, Marone Luke, Ali Abbas
Department of Cardiology, Michigan State University/Sparrow Hospital, 1215 E Michigan Ave, Lansing, MI 48912, USA.
Department of Neurology, Emory University, Atlanta, GA 30322, USA.
Diagnostics (Basel). 2020 Jul 24;10(8):515. doi: 10.3390/diagnostics10080515.
Peripheral arterial disease (PAD) involves arterial blockages in the body, except those serving the heart and brain. We explore the relationship of functional limitation and PAD symptoms obtained from a quality-of-life questionnaire about the severity of the disease. We used a supervised artificial intelligence-based method of data analyses known as machine learning (ML) to demonstrate a nonlinear relationship between symptoms and functional limitation amongst patients with and without PAD.
This paper will demonstrate the use of machine learning to explore the relationship between functional limitation and symptom severity to PAD severity.
We performed supervised machine learning and graphical analysis, analyzing 703 patients from an administrative database with data comprising the toe-brachial index (TBI), baseline demographics and symptom score(s) derived from a modified vascular quality-of-life questionnaire, calf circumference in centimeters and a six-minute walk (distance in meters).
Graphical analysis upon categorizing patients into critical limb ischemia (CLI), severe PAD, moderate PAD and no PAD demonstrated a decrease in walking distance as symptoms worsened and the relationship appeared nonlinear. A supervised ML ensemble (random forest, neural network, generalized linear model) found symptom score, calf circumference (cm), age in years, and six-minute walk (distance in meters) to be important variables to predict PAD. Graphical analysis of a six-minute walk distance against each of the other variables categorized by PAD status showed nonlinear relationships. For low symptom scores, a six-minute walk test (6MWT) demonstrated high specificity for PAD.
PAD patients with the greatest functional limitation may sometimes be asymptomatic. Patients without PAD show no relationship between functional limitation and symptoms. Machine learning allows exploration of nonlinear relationships. A simple linear model alone would have overlooked or considered such a nonlinear relationship unimportant.
外周动脉疾病（PAD）涉及身体动脉阻塞，但不包括供应心脏和大脑的动脉。我们通过一份关于疾病严重程度的生活质量问卷，探究功能受限与PAD症状之间的关系。我们使用一种基于人工智能的监督式数据分析方法，即机器学习（ML），来证明有和没有PAD的患者症状与功能受限之间的非线性关系。
本文将展示如何使用机器学习来探究功能受限与症状严重程度对PAD严重程度的关系。
我们进行了监督式机器学习和图形分析，分析了来自管理数据库的703名患者的数据，包括踝臂指数（TBI）、基线人口统计学数据以及从改良的血管生活质量问卷得出的症状评分、以厘米为单位测量的小腿周长和六分钟步行距离（以米为单位）。
将患者分为严重肢体缺血（CLI）、重度PAD、中度PAD和无PAD进行图形分析，结果显示随着症状加重，步行距离缩短，且这种关系呈非线性。一个监督式ML集成模型（随机森林、神经网络、广义线性模型）发现症状评分、小腿周长（厘米）、年龄（岁）和六分钟步行距离（米）是预测PAD的重要变量。按PAD状态分类，对六分钟步行距离与其他每个变量进行图形分析，显示出非线性关系。对于低症状评分，六分钟步行试验（6MWT）对PAD具有高特异性。
功能受限最严重的PAD患者有时可能无症状。无PAD的患者功能受限与症状之间无关联。机器学习能够探究非线性关系。仅一个简单的线性模型可能会忽略或认为这种非线性关系不重要。
