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


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.

Publication DOI: https://doi.org/10.1007/s00521-022-07049-z
Divisions: College of Business and Social Sciences > Aston Business School
College of Business and Social Sciences > Aston Business School > Operations & Information Management
Additional Information: © 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.
Uncontrolled Keywords: Diabetes mellitus,Interpretable artificial intelligence,Machine learning,The Internet of Medical Things (IoMT),Software,Artificial Intelligence
Publication ISSN: 1433-3058
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://link.sp ... 521-022-07049-z (Publisher URL)
PURE Output Type: Article
Published Date: 2022-03-24
Published Online Date: 2022-03-24
Accepted Date: 2022-01-31
Authors: Chang, Victor (ORCID Profile 0000-0002-8012-5852)
Bailey, Jozeene
Xu, Qianwen Ariel
Sun, Zhili



Version: Accepted Version

License: ["licenses_description_other" not defined]

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