System to reduce the rate of patients not-adherence to medical treatment for diabetes using Machine Learning

Vasquez Silva N.; Arroyo Solis J.; Aliaga Cerna E. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2025) · DOI: 10.18687/laccei2025.1.1.457

Resumen

Measuring adherence to medical treatment in diabetic patients can be a costly and time-consuming process, with commonly used methods including pill counts, self-report questionnaires, and daily reminder phone calls. Based on this, we propose a mobile system that utilizes a supervised predictive Machine Learning algorithm. This system reduces the analysis period while identifying the probability of non-compliance with medical treatment. Furthermore, it permits physicians to monitor and control their patients conveniently and intuitively. Our proposal underwent validation with diabetes care and prevention experts, as well as patients. The study findings indicated that 72% of adult diabetes patients were able to enhance their adherence to prescribed treatments through utilizing the mobile application.

Instituciones: Universidad Peruana de Ciencias Aplicadas