Performance Evaluation of Machine Learning Algorithms for Sarcopenia Diagnosis in Older Adults

Abstract

Background: 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. Methods: 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. Results: 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. Conclusions: 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).

Machine learning for predicting hepatitis B or C virus infection in diabetic patients

“…Sixth, in the current study, we exclusively assessed existing machine learning models rather than undertaking the development of a new model. Through comprehensive searches of previous studies evaluating machine learning models predicting outcomes related to hepatitis, we finally selected four machine learning models shown higher performance metrics rather than other algorithms, which followed approach taken in a previous study 69 . Furthermore, it is important to note that surpassing the performance of established models falls beyond the scope of our research.…”

“…We evaluated four machine-learning models to predict the risk of HBV or HCV infection among patients with diabetes: RF, SVM, XGBoost, and LASSO, which were shown higher performance metrics compared to other algorithms in previous studies for predicting outcomes related to hepatitis 26,29,34,69 . To achieve the best performance, we conducted hyperparameter tuning for each algorithm (Table 4).…”