Multilayer perceptron modeling of health literacy and medication adherence determinants in Arab patients with cardiometabolic disease: the role of quality of life, eating behaviors, and physical activity

Cardiometabolic diseases (CMDs) are a leading cause of global mortality, with an increasing prevalence in the Arab world, particularly among older patients. This pioneer study using the MLP modeling technique will investigate the determinants of health literacy and medical adherence among Arab CMD patients, focusing specifically on quality of life, eating behaviors, and physical activity. Here we conducted study on Arab patients with cardiometabolic diseases using validated instruments to assess medication adherence, health literacy, quality of life, eating behavior, and physical activity and observed the relationships between them using machine learning modelling technique. The study demonstrated that 82.4% of participants reported low medication adherence, and 39.8% had adequate health literacy. Physical activity had a weak positive correlation with medication adherence while participants with higher adherence were somewhat more health-literate but reported lower enjoyment of food. The study observed using the MLP model that the medical adherence and the health literacy models demonstrated strong classification performance for predictors like quality of life, hunger, emotional overeating, and satiety responsiveness. The study highlighted that the ML model identified quality of life, eating behavior, and physical activity as key predictors for health literacy and medication adherence among Arab cardiometabolic patients.