Disease diagnosis using machine learning techniques: a review and classification

  • Mehrbakhsh Nilashi
  • , Neda Ahmadi
  • , Sarminah Samad
  • , Leila Shahmoradi
  • , Hossein Ahmadi
  • , Othman Ibrahim
  • , Shahla Asadi
  • , Rusli Abdullah
  • , Rabab Ali Abumalloh
  • , Elaheh Yadegari

    Research output: Contribution to journalArticlepeer-review

    Abstract

    In this research, we reviewed and classified academic conference and journal papers; which used data mining techniques in disease classification and diagnosis based on public medical datasets published between 2007 and 2019. The results of this review demonstrated that the application of data mining techniques in disease classification has experienced a dramatic rise in recent years. The finding of this paper also revealed that there was minimal focus on developing methods using incremental version of data mining techniques. We hope that this research will provide useful information about various data mining techniques, their application in disease diagnosis, and help researchers in developing medical decision support systems with insights into the state-of-the-art of development methods.
    Original languageEnglish
    Pages (from-to)19-30
    JournalJournal of Soft Computing and Decision Support Systems
    Volume7
    Issue number1
    Early online date12 Mar 2020
    Publication statusPublished - 12 Mar 2020

    Keywords

    • Data mining
    • public medical datasets
    • diseases diagnosis
    • literature survey
    • UCI
    • PRISMA
    • Biological sciences

    Fingerprint

    Dive into the research topics of 'Disease diagnosis using machine learning techniques: a review and classification'. Together they form a unique fingerprint.

    Cite this