ArticleMedicine2019
Sarcopenia feature selection and risk prediction using machine learning: A cross-sectional study.
Article in Medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it, 37 citations in OpenAlex.
- Risk prediction model performances for sarcopenia in middle-aged and elderly people: a systematic review and meta-analysis.BMC musculoskeletal disorders · 2026Pooled it
- Article
- Predictive model development for possible sarcopenia in community-dwelling older adults: a cross-sectional machine learning approach using the Korean frailty and aging cohort study.BMC geriatrics · 2025Article
- Identifying risk factors for sarcopenia using machine learning: insights from multimodal data.European geriatric medicine · 2025Article
- Anthropometric Measurements for Predicting Low Appendicular Lean Mass Index for the Diagnosis of Sarcopenia: A Machine Learning Model.Journal of functional morphology and kinesiology · 2025Article
- Towards Precision in Sarcopenia Assessment: The Challenges of Multimodal Data Analysis in the Era of AI.International journal of molecular sciences · 2025Review
- Risk factors for postoperative pulmonary complications in non-adenocarcinoma non-small cell lung cancer patients undergoing surgery after neoadjuvant therapy.Translational lung cancer research · 2025Article
- Prediction of sarcopenia at different time intervals: an interpretable machine learning analysis of modifiable factors.BMC geriatrics · 2025Article
- Identification of Predictors of Sarcopenia in Older Adults Using Machine Learning: English Longitudinal Study of Ageing.Journal of clinical medicine · 2024Article
- Machine Learning-Based Identification of Diagnostic Biomarkers for Korean Male Sarcopenia Through Integrative DNA Methylation and Methylation Risk Score: From the Korean Genomic Epidemiology Study (KoGES).Journal of Korean medical science · 2024Article
- Metaheuristic-Based Feature Selection Methods for Diagnosing Sarcopenia with Machine Learning Algorithms.Biomimetics (Basel, Switzerland) · 2024Article
- Sarcopenia prediction using shear-wave elastography, grayscale ultrasonography, and clinical information with machine learning fusion techniques: feature-level fusion vs. score-level fusion.Scientific reports · 2024Article
- Sarcopenia diagnosis using skeleton-based gait sequence and foot-pressure image datasets.Frontiers in public health · 2024Article
- Article
- Article
- Machine Learning for Sarcopenia Prediction in the Elderly Using Socioeconomic, Infrastructure, and Quality-of-Life Data.Healthcare (Basel, Switzerland) · 2023Article
- Performance Evaluation of Machine Learning Algorithms for Sarcopenia Diagnosis in Older Adults.Healthcare (Basel, Switzerland) · 2023Article
- Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey.Healthcare (Basel, Switzerland) · 2023Article
- Sarcopenia Prediction for Elderly People Using Machine Learning: A Case Study on Physical Activity.Healthcare (Basel, Switzerland) · 2023Article
- A Machine-Learning Approach to Target Clinical and Biological Features Associated with Sarcopenia: Findings from Northern and Southern Italian Aging Populations.Metabolites · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of this study was to verify the usefulness of machine learning (ML) for selection of risk factors and development of predictive models for patients with sarcopenia.We collected medical records from Korean postmenopausal women based on Korea National Health and Nutrition Examination Surveys. A training data set compiled from simple survey data was used to construct models based on popular ML algorithms (e.g., support vector machine, random forest [RF], and logistic regression).A total of 4020 patients ≥65 years of age were enrolled in this study. The study population consisted of 1698 (42.2%) male and 2322 (57.8%) female patients. The 10 most important risk factors in men were body mass index (BMI), red blood cell (RBC) count, blood urea nitrogen (BUN), vitamin D, ferritin, fiber intake (g/d), primary diastolic blood pressure, white blood cell (WBC) count, fat intake (g/d), age, glutamic-pyruvic transaminase, niacin intake (mg/d), protein intake (g/d), fasting blood sugar, and water intake (g/d). The 10 most important risk factors in women were BMI, water intake (g/d), WBC, RBC count, iron intake (mg/d), BUN, high-density lipoprotein, protein intake (g/d), fiber consumption (g/d), vitamin C intake (mg/d), parathyroid hormone, niacin intake (mg/d), carotene intake (μg/d), potassium intake (mg/d), calcium intake (mg/d), sodium intake (mg/d), retinol intake (μg/d), and age. A receiver operating characteristic (ROC) curve analysis found that the area under the ROC curve for each ML model was not significantly different within a gender.The most cost-effective method in clinical practice is to make feature selection using RF models and expert knowledge and to make disease prediction using verification by several ML models. However, the developed prediction model should be validated using additional studies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.