Sarcopenia feature selection and risk prediction using machine learning A cross-sectional study

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 bodymass 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 (mg/d), potassiumintake (mg/d), calcium intake (mg/d), sodiumintake (mg/d), retinol intake (mg/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.