Logistic Regression Modeling to Predict Sarcopenia Frailty among Aging Adults

Abstract

Sarcopenia and frailty have been associated with low aging population capacities for exercise and high metabolic instability. To date, the current models merely support one classification with an accuracy of 83%. The models also reflect overfitting dataset complexities in predicting the accuracy and detecting the misclassifications of rare diseases. As multiple classifications led to incongruent data analyses and methods, each evaluation yielded inaccurate results regarding high prediction accuracy. This study intends to contribute to the current medical informatics literature by comparing the most optimal model to identify relevant patterns and parameters for prediction model development. The methods were duly assessed on a real dataset together with the classification model. Meanwhile, the obesity physical frailty (OPF) model was presented as a conceptual study model. A matrix of accuracy, classification, and feature selection was also utilized to compare the computer output and deep learning models against current counterparts. Essentially, the study findings predicted that an individuals' risk of sarcopenia corresponded to physical frailty. Each model was compared with an accuracy matrix to determine the best-fitting model. Resultantly, logistic regression produced the highest results with an accuracy rate of 97.69% compared to the other four study models.

Machine Learning Applications in Sarcopenia Detection and Management: A Comprehensive Survey

“…Logistic regression has emerged as a powerful statistical technique for predicting sarcopenia risk and onset amongst aging populations. By incorporating multiple predictors like age, sex, body composition, and physical activity, logistic models can estimate the individual likelihood of developing sarcopenia [ 100 , 101 , 102 ]. These predictive models enable more targeted screening and early interventions.…”

“…A notable study by Kaur et al [ 100 ] demonstrates the potential of logistic regression in evaluating sarcopenia and frailty. While specific variables were not defined, their model achieved a remarkable 97.69% accuracy in predicting outcomes.…”

Machine Learning for Sarcopenia Prediction in the Elderly Using Socioeconomic, Infrastructure, and Quality-of-Life Data

“…Several studies explore key features across all possible criteria and train the machine with them. For example, Kaur et al [41] developed the logistic regression model using obesity-related features such as PA, protein intake, fat, diabetes, and body composition, achieving almost 97.69% accuracy in predicting outcomes as it is known that the obesityrelated features contribute highly to prediction accuracy. Kang et al [42] extracted features of intake information and nutrition status by using random forest and achieved an AUC value of about 0.8 across the linear regression (LR), gradient boost (GB), and support vector machine (SVM) algorithms.…”