Forthcoming Articles

International Journal of Electronic Healthcare

International Journal of Electronic Healthcare (IJEH)

Forthcoming articles have been peer-reviewed and accepted for publication but are pending final changes, are not yet published and may not appear here in their final order of publication until they are assigned to issues. Therefore, the content conforms to our standards but the presentation (e.g. typesetting and proof-reading) is not necessarily up to the Inderscience standard. Additionally, titles, authors, abstracts and keywords may change before publication. Articles will not be published until the final proofs are validated by their authors.

Forthcoming articles must be purchased for the purposes of research, teaching and private study only. These articles can be cited using the expression "in press". For example: Smith, J. (in press). Article Title. Journal Title.

Articles marked with this shopping trolley icon are available for purchase - click on the icon to send an email request to purchase.

Online First articles are also listed here. Online First articles are fully citeable, complete with a DOI. They can be cited, read, and downloaded. Online First articles are published as Open Access (OA) articles to make the latest research available as early as possible.

Open AccessArticles marked with this Open Access icon are Online First articles. They are freely available and openly accessible to all without any restriction except the ones stated in their respective CC licenses.

Register for our alerting service, which notifies you by email when new issues are published online.

International Journal of Electronic Healthcare (One paper in press)

Regular Issues

  • Retrieval optimisation in case-based reasoning systems applied to healthcare   Order a copy of this article
    by Seema Sharma, Deepti Mehrotra, Narjès Bellamine Ben Saoud 
    Abstract: Case-based reasoning (CBR) is a cognitive approach that solves new problems by referencing solutions from similar past cases. It is particularly effective in domains where knowledge is incomplete and exceptions are common, such as medical decision-making. This study focuses on enhancing the retrieval phase of CBR systems that manage large-scale medical case bases. To achieve this, various k-nearest neighbour (KNN) algorithm variants were evaluated to identify the most suitable for medical applications. The study compared standard KNN, fuzzy KNN (F-KNN), fuzzy k-nearest centroid neighbour (F-KNCN), Bonferroni mean-based fuzzy KNN (BMF-KNN), Bonferroni mean-based fuzzy KNCN (BMF-KNCN), and random KNN (R-KNN) algorithms. These were tested on four benchmark heart disease datasets within a CBR framework. The results demonstrate that R-KNN achieves the highest accuracy of 92%, outperforming the other variants. Thus, R-KNN is identified as the most effective retrieval method for improving medical CBR systems.
    Keywords: case-based reasoning; CBR; random k-nearest neighbours algorithm; k-nearest neighbour; K-NN; fuzzy k-nearest neighbour; F-KNN; fuzzy k-nearest centroid neighbour; F-KNN; Bonferroni mean-based fuzzy k nearest neighbour; BMF-KNN; Bonferroni mean-based fuzzy k-nearest centroid neighbour; BMF-KNCN.
    DOI: 10.1504/IJEH.2025.10073160
     

 

Return to top