A Machine Learning Approach to Predict the Risk of Sarcopenia

Abstract

As the world’s population is aging, the number of people suffering from sarcopenia is rising rapidly. Hence this study aimed to propose an approach that can be used to predict the risk of sarcopenia in non-clinical settings. A total of 90 participants were devided into three study groups: 30 participants with low-sarcopenic risk, 30 with mid-sarcopenic risk, and 30 with high-sarcopenic risk. Each participant was instructed to sit on a chair, and a device equipped with an electrical stimulator, a surface electromyogram acquisition module, and an electrode module was attached to the skin surface on the rectus femoris and biceps femoris muscles of the dominant leg. While delivering multi-frequency electrical stimulation to the muscles, the device measured muscle response signals. Ten parameters to quantify nonlinearity in time-series data were time-sequentially extracted from the measured signals. The sequences of the parameters were fed into a bidirectional long short-term memory layer, and then the output was integrated with demographic characteristics by a feature fusion layer. The 80% of participants in each study group were used for classifier training and validation, and the remaining 20% of participants in each study group were used to test the classifier. The classifier achieved a test accuracy of 0.944 in classifying the low-, mid-, and high-sarcopenic risk groups. This study would pave the way for in-home self-monitoring of sarcopenic risk, which can contribute to early and effective prevention of sarcopenia.