Ozgur Su, Altinok Yasemin Atik, Bozkurt Devrim, Saraç Zeliha Fulden, Akçiçek Selahattin Fehmi
Department of Biostatistics and Medical Informatics, Faculty of Medicine, Ege University, 35040 Izmir, Turkey.
Translational Pulmonary Research Center-EgeSAM, Ege University, 35040 Izmir, Turkey.
Healthcare (Basel). 2023 Oct 9;11(19):2699. doi: 10.3390/healthcare11192699.
Sarcopenia is a progressive and generalized skeletal muscle disorder. Early diagnosis is necessary to reduce the adverse effects and consequences of sarcopenia, which can help prevent and manage it in a timely manner. The aim of this study was to identify the important risk factors for sarcopenia diagnosis and compare the performance of machine learning (ML) algorithms in the early detection of potential sarcopenia.
A cross-sectional design was employed for this study, involving 160 participants aged 65 years and over who resided in a community. ML algorithms were applied by selecting 11 features-sex, age, BMI, presence of hypertension, presence of diabetes mellitus, SARC-F score, MNA score, calf circumference (CC), gait speed, handgrip strength (HS), and mid-upper arm circumference (MUAC)-from a pool of 107 clinical variables. The results of the three best-performing algorithms were presented.
The highest accuracy values were achieved by the ALL (male + female) model using LightGBM (0.931), random forest (RF; 0.927), and XGBoost (0.922) algorithms. In the female model, the support vector machine (SVM; 0.939), RF (0.923), and k-nearest neighbors (KNN; 0.917) algorithms performed the best. Regarding variable importance in the ALL model, the last HS, sex, BMI, and MUAC variables had the highest values. In the female model, these variables were HS, age, MUAC, and BMI, respectively.
Machine learning algorithms have the ability to extract valuable insights from data structures, enabling accurate predictions for the early detection of sarcopenia. These predictions can assist clinicians in the context of predictive, preventive, and personalized medicine (PPPM).
肌肉减少症是一种进行性全身性骨骼肌疾病。早期诊断对于减少肌肉减少症的不良影响和后果至关重要，这有助于及时预防和管理该疾病。本研究的目的是确定肌肉减少症诊断的重要风险因素，并比较机器学习（ML）算法在早期检测潜在肌肉减少症方面的性能。
本研究采用横断面设计，纳入了160名居住在社区的65岁及以上参与者。通过从107个临床变量中选择11个特征——性别、年龄、体重指数（BMI）、高血压病史、糖尿病病史、SARC-F评分、微型营养评定法（MNA）评分、小腿围度（CC）、步速、握力（HS）和上臂中部围度（MUAC）——应用ML算法。展示了三种性能最佳算法的结果。
使用LightGBM算法的ALL（男性+女性）模型（0.931）、随机森林（RF；0.927）和XGBoost算法（0.922）实现了最高准确率值。在女性模型中，支持向量机（SVM；0.939）、RF（0.923）和k近邻（KNN；0.917）算法表现最佳。关于ALL模型中的变量重要性，最后几个HS、性别、BMI和MUAC变量的值最高。在女性模型中，这些变量分别是HS、年龄、MUAC和BMI。
机器学习算法有能力从数据结构中提取有价值的见解，从而对肌肉减少症的早期检测进行准确预测。这些预测可在预测、预防和个性化医学（PPPM）背景下协助临床医生。
