A fusion-based machine learning approach for the prediction of the onset of diabetes

Muhammad Waqas Nadeem, Hock Guan Goh, Vasaki Ponnusamy, Ivan Andonovic, Muhammad Adnan Khan, Muzammil Hussain

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Abstract

A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.
Original languageEnglish
Article number1393
Number of pages17
JournalHealthcare
Volume9
Issue number10
Early online date18 Oct 2021
DOIs
Publication statusPublished - 18 Oct 2021

Keywords

  • diabetes prediction
  • machine learning
  • healthcare applications
  • support vector machines;
  • Artificial Neural Networks
  • data fusion
  • intelligent system

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