The Web platform for diabetes prediction using weighted machine learning techniques based on personal and clinical indicators

Authors

  • Viridiana Barrera Soto Unidad Académica Multidisciplinaria Reynosa Rodhe – Universidad Autónoma de Tamaulipas
  • Juan Carlos Huerta Mendoza Unidad Académica Multidisciplinaria Reynosa Rodhe – Universidad Autónoma de Tamaulipas.
  • José Lázaro Martínez Unidad Académica Multidisciplinaria Reynosa Rodhe – Universidad Autónoma de Tamaulipas. https://orcid.org/0000-0002-1276-6957

DOI:

https://doi.org/10.61467/2007.1558.2026.v17i2.1190

Keywords:

Artificial Intelligence, Predictive model, Machine Learning, Diabetes

Abstract

Diabetes is a chronic metabolic disease characterized by elevated levels of glucose in the blood (or blood sugar), which over time leads to severe damage to the heart, blood vessels, eyes, kidneys, and nerves. The most common type is type 2 diabetes, usually in adults, which occurs when the body becomes resistant to insulin or does not produce enough insulin. By using artificial intelligence (AI) techniques in complex problems such as disease diagnosis, a degree of certainty in the results has been achieved to identify a specific type of disease. These applications have been advantageous because large amounts of patient data can be analyzed to find patterns. This work proposes a platform for the prediction of type 2 diabetes based on clinical or personal indicators. To do this, two supervised classification models were constructed using the PIMA Indian Diabetes dataset and the Centers for Disease Control and Prevention (CDC) dataset, integrating both into a web platform for prediction with new data to support the decisions of doctors and healthcare professionals. By integrating different algorithms into the final predictive model through voting weighting, the accuracy percentage in prediction has been increased.

 

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Published

2026-02-16

How to Cite

Barrera Soto, V., Huerta Mendoza, J. C., & Lázaro Martínez, J. (2026). The Web platform for diabetes prediction using weighted machine learning techniques based on personal and clinical indicators. International Journal of Combinatorial Optimization Problems and Informatics, 17(2), 312–323. https://doi.org/10.61467/2007.1558.2026.v17i2.1190

Issue

Vol. 17 No. 2 (2026)

Section

Articles

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