Predictive machine learning applying cross industry standard process for data mining for the diagnosis of diabetes mellitus type 2

• Autores: Garcia-Rios, Victor, Marres-Salhuana, Marieta, Sierra-Liñan, Fernando, Cabanillas-Carbonell, Michael
• Formato: artículo
• Fecha de Publicación: 2023
• Institución: Universidad Autónoma del Perú
• Repositorio: AUTONOMA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.autonoma.edu.pe:20.500.13067/2872
• Enlace del recurso: https://hdl.handle.net/20.500.13067/2872; https://doi.org/10.11591/ijai.v12.i4.pp1713-1726
• Nivel de acceso: acceso abierto
• Materia: Diagnosis; Machine learning; Prediction; Random forest; Type 2 diabetes mellitus; https://purl.org/pe-repo/ocde/ford#2.02.04

Currently, type 2 diabetes mellitus is one of the world's most prevalent diseases and has claimed millions of people's lives. The present research aims to know the impact of the use of machine learning in the diagnostic process of type 2 diabetes mellitus and to offer a tool that facilitates the diagnosis of the dis-ease quickly and easily. Different machine learning models were designed and compared, being random forest was the algorithm that generated the model with the best performance (90.43% accuracy), which was integrated into a web platform, working with the PIMA dataset, which was validated by specialists from the Peruvian League for the Fight against Diabetes organization. The result was a decrease of (A) 88.28% in the information collection time, (B) 99.99% in the diagnosis time, (C) 44.42% in the diagnosis cost, and (D) 100% in the level of difficulty, concluding that the application of machine learning can significantly optimize the diagnostic process of type 2 diabetes mellitus.