A Recommendation System of Nutrition and Physical Activity for Patients with Type 2 Diabetes Mellitus

• João Godinho ^[1] ; Sara Batista ^[1] ; Diogo Martinho ^[1] ; Luís Conceição ^{[1] [2]}
  1. [1] Instituto Politécnico do Porto

     Instituto Politécnico do Porto

     Santo Ildefonso, Portugal

     
  2. [2] Universidade do Minho

     Universidade do Minho

     Braga (São José de São Lázaro), Portugal

     
• Localización: Intelligent Data Engineering and Automated Learning – IDEAL 2020. 21st International Conference: Guimarães, Portugal; November 4–6, 2020. Proceedings / Cesar Analide (ed. lit.), Paulo Novais (ed. lit.), David Camacho Fernández (ed. lit.), Hujun Yin (ed. lit.), Vol. 2, 2020 (Part II), ISBN 978-3-030-62365-4, págs. 287-297
• Idioma: inglés
• Enlaces
  • Texto completo
• Resumen

  • The diseases related to how we eat are a major public health concern and continue to endanger the health of the population, as well as the sustainability of health systems due to the significant increase in the burden of anti-diabetic drugs as well as new therapeutic classes that have a high cost. An unbalanced diet can result in metabolic disorders, malnutrition, overweight, mental problems and other medical risk factors, such as cardiovascular disease, type 2 diabetes mellitus or, in the worst case, cancer. In 2019, according to the International Diabetes Federation statistic, the estimated prevalence of diabetes in the world population over the age of 20 was 10.4%, covering about 463 million people. Therefore, there is a need for a greater care and attention for this disease, both in terms of the respective treatment and prevention. This kind of need is dependent of the decisions that either the patient or the health professional make daily. With the rise of big data and data analysis technologies, recommendation systems have become essential and necessary for a custom data management, according to the user. These systems play a key role because of its ability to increase the amount of information available. In this paper, we propose a conceptual definition of a recommendation system that aims to assist the monitoring of Type 2 Diabetes Mellitus disease thus enabling a more effective management of the disease.