Automatic Classification of Sarcopenia Level in Older Adults: A Case Study at Tijuana General Hospital
Automatic Classification of Sarcopenia Level in Older Adults: A Case Study at Tijuana General Hospital
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DOI：
10.3390/ijerph16183275
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发表时间：
2019-09-02
影响因子：
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通讯作者：
Zuniga, Clemente
中科院分区：
文献类型：
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作者：
Castillo-Olea, Cristian;Garcia-Zapirain Soto, Begonya;Zuniga, Clemente
本文提出了一项基于老年人肌少症水平数据分析的研究。肌肉减少症是50岁左右成年人的一种常见病症，肌肉质量每年减少1%至2%，肌力在50岁至60岁之间每年减少1.5%，随后每年增加3%。世界卫生组织估计，60 至 70 岁的人中有 5-13% 以及 80 岁或以上的人有 11-50% 患有肌少症。这项研究涉及 166 名患者和 99 个变量。人口统计数据包括年龄、性别、居住地、受教育程度、婚姻状况、教育水平、收入、职业和下加利福尼亚州的财政支持，并确定了血糖、胆固醇血症和甘油三酯血症等生化参数。共有166名患者参与了这项研究，平均年龄为77.24岁。该研究的目的是除了确定研究中使用的每个变量的权重之外，还利用人工智能提供老年人肌少症水平的自动分类器。我们在这项工作中使用了机器学习技术，其中使用了 10 个分类器来评估变量并确定哪个可以提供最佳结果，即最近邻 (3)、线性 SVM（支持向量机）(C = 0.025)、RBF（径向基函数）SVM (gamma = 2, C = 1)、高斯过程 (RBF (1.0))、决策树 (max_深度 = 3)、随机森林（max_depth=3，n_estimators = 10）、MPL（多层感知器）（alpha = 1）、AdaBoost、高斯朴素贝叶斯和 QDA（二次判别分析）。由变量排名平均值确定的特征选择表明，年龄、收缩性动脉高血压 (HAS)、迷你营养评估 (MNA)、慢性疾病数量 (ECNumber) 和钠是确定肌少症水平的五个最重要的变量，因此在建立任何治疗或预防措施之前非常重要。对中度和重度肌肉减少症中使用的变量和分类器之间存在的关系的分析表明，使用带有年龄、HAS、MNA、ECNumber 和钠变量的 RBF SVM 分类器的肌肉减少症水平具有 82'5 的准确度、90'2 F1 和 82'8 的精度。
This paper presents a study based on data analysis of the sarcopenia level in older adults. Sarcopenia is a prevalent pathology in adults of around 50 years of age, whereby the muscle mass decreases by 1 to 2% a year, and muscle strength experiences an annual decrease of 1.5% between 50 and 60 years of age, subsequently increasing by 3% each year. The World Health Organisation estimates that 5-13% of individuals of between 60 and 70 years of age and 11-50% of persons of 80 years of age or over have sarcopenia. This study was conducted with 166 patients and 99 variables. Demographic data was compiled including age, gender, place of residence, schooling, marital status, level of education, income, profession, and financial support from the State of Baja California, and biochemical parameters such as glycemia, cholesterolemia, and triglyceridemia were determined. A total of 166 patients took part in the study, with an average age of 77.24 years. The purpose of the study was to provide an automatic classifier of sarcopenia level in older adults using artificial intelligence in addition to identifying the weight of each variable used in the study. We used machine learning techniques in this work, in which 10 classifiers were employed to assess the variables and determine which would provide the best results, namely, Nearest Neighbors (3), Linear SVM (Support Vector Machines) (C = 0.025), RBF (Radial Basis Function) SVM (gamma = 2, C = 1), Gaussian Process (RBF (1.0)), Decision Tree (max_depth = 3), Random Forest (max_depth=3, n_estimators = 10), MPL (Multilayer Perceptron) (alpha = 1), AdaBoost, Gaussian Naive Bayes, and QDA (Quadratic Discriminant Analysis). Feature selection determined by the mean for the variable ranking suggests that Age, Systolic Arterial Hypertension (HAS), Mini Nutritional Assessment (MNA), Number of chronic diseases (ECNumber), and Sodium are the five most important variables in determining the sarcopenia level, and are thus of great importance prior to establishing any treatment or preventive measure. Analysis of the relationships existing between the presence of the variables and classifiers used in moderate and severe sarcopenia revealed that the sarcopenia level using the RBF SVM classifier with Age, HAS, MNA, ECNumber, and Sodium variables has 82'5 accuracy, a 90'2 F1, and 82 ' 8 precision.