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

Victor Garcia-Rios, Marieta Marres-Salhuana, Fernando Sierra-Liñan, Michael Cabanillas-Carbonell


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.


Diagnosis; Machine learning; Prediction; Random forest; Type 2 diabetes mellitus

Full Text:




  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

IAES International Journal of Artificial Intelligence (IJ-AI)
ISSN/e-ISSN 2089-4872/2252-8938 
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

View IJAI Stats