Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms

Victor Chang*, Jozeene Bailey, Qianwen Ariel Xu, Zhili Sun

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


This paper proposes an e-diagnosis system based on machine learning (ML) algorithms to be implemented on the Internet of Medical Things (IoMT) environment, particularly for diagnosing diabetes mellitus (type 2 diabetes). However, the ML applications tend to be mistrusted because of their inability to show the internal decision-making process, resulting in slow uptake by end-users within certain healthcare sectors. This research delineates the use of three interpretable supervised ML models: Naïve Bayes classifier, random forest classifier, and J48 decision tree models to be trained and tested using the Pima Indians diabetes dataset in R programming language. The performance of each algorithm is analyzed to determine the one with the best accuracy, precision, sensitivity, and specificity. An assessment of the decision process is also made to improve the model. It can be concluded that a Naïve Bayes model works well with a more fine-tuned selection of features for binary classification, while random forest works better with more features.

Original languageEnglish
JournalNeural Computing and Applications
Early online date24 Mar 2022
Publication statusE-pub ahead of print - 24 Mar 2022

Bibliographical note

© 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use [https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms], but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s00521-022-07049-z

Funding Information:
This research is partly supported by VC Research (VCR 0000159) for Prof Chang.


  • Diabetes mellitus
  • Interpretable artificial intelligence
  • Machine learning
  • The Internet of Medical Things (IoMT)


Dive into the research topics of 'Pima Indians diabetes mellitus classification based on machine learning (ML) algorithms'. Together they form a unique fingerprint.

Cite this