A Machine Learning Approach to Predicting Physical Activity Levels in Adolescents

The ongoing evolution of technology has had both positive and negative effects on modern society. On the positive side, it has significantly improved the ease with which various activities can be performed. However, it has also had a negative impact by reducing physical activity. This reduction in physical activity, in turn, increases the risk of chronic diseases that contribute to global mortality rates. This research aims to assess the effectiveness of machine learning in predicting the physical activity levels of adolescents. The study utilizes data from accelerometers, specifically the ActiGraph GT3X. The research methodology employs a semi-supervised machine learning approach, using the support vector machine and decision tree algorithms to make these predictions. The sample comprises 61 adolescents (males = 17, female = 44), including high school students and university students aged 18-21, from the West Java region. The results from the machine learning model using the decision tree algorithm indicated a model accuracy of 97.50% in predicting physical activity levels. In contrast, the accuracy obtained from the performance analysis using the confusion matrix for the support vector machine model was 92.5%. Based on these accuracy levels, the decision tree algorithm outperforms the support vector machine algorithm's accuracy. Further analyses involving different models are necessary to determine which algorithm offers the highest level of accuracy