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

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AudienciaPúblico en generales_ES
CoberturaMéxicoes_ES
Fecha de ingreso2026-10-05T16:32:05Z
Fecha de publicación2026-01-01
ResumenDiabetes 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.es_ES
Doihttps://doi.org/10.61467/2007.1558.2026.v17i2.1190es_ES
URIhttps://riuat.uat.edu.mx/handle/123456789/3946
Idiomaenes_ES
EditorialINT JOURNAL COMBINATORIAL OPTIMIZATION PROBLEMS \& INFORMATICSes_ES
RelaciónInternational Journal of Combinatorial Optimization Problems and Informaticses_ES
URL relacionadohttps://doi.org/10.61467/2007.1558.2026.v17i2.1190es_ES
DerechosAcceso abierto (Metadatos de producción científica)es_ES
Licenciahttp://purl.org/coar/access_right/c_abf2es_ES
FuenteInternational Journal of Combinatorial Optimization Problems and Informatics
Palabra claveArtificial intelligencees_ES
Palabra clavepredictive modelses_ES
Palabra clavemachine learninges_ES
Palabra clavediabeteses_ES
TítuloWeb platform for diabetes prediction using weighted machine learning techniques based on personal and clinical indicatorses_ES
TipoArtículoes_ES
ArbitradoHa sido Arbitradoes_ES
AutorSoto, Viridiana Barrera
AutorMendoza, Juan Carlos Huerta
AutorMartinez, Jose Lazaro
AutorSoto, Viridiana Barreraes_ES
AutorMendoza, Juan Carlos Huertaes_ES
AutorMartinez, Jose Lazaroes_ES
InstituciónUniversidad Autónoma de Tamaulipas
InstituciónUniversidad Autónoma de Tamaulipases_ES
Número2es_ES
Rango de páginas312-323es_ES
URL relacionadahttps://doi.org/10.61467/2007.1558.2026.v17i2.1190
Tipo de artículoIndexado
Tipo de artículoIndexadoes_ES
Volumen17es_ES

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