Theory and practice of integrating machine learning and conventional statistics in medical data analysis

Sarinder Kaur Dhillon, Mogana Darshini Ganggayah, Siamala Sinnadurai, Pietro Lio, Nur Aishah Taib

Research output: Contribution to journalReview ArticleResearchpeer-review

4 Citations (Scopus)

Abstract

The practice of medical decision making is changing rapidly with the development of innovative computing technologies. The growing interest of data analysis with improvements in big data computer processing methods raises the question of whether machine learning can be integrated with conventional statistics in health research. To help address this knowledge gap, this paper presents a review on the conceptual integration between conventional statistics and machine learning, focusing on the health research. The similarities and differences between the two are compared using mathematical concepts and algorithms. The comparison between conventional statistics and machine learning methods indicates that conventional statistics are the fundamental basis of machine learning, where the black box algorithms are derived from basic mathematics, but are advanced in terms of automated analysis, handling big data and providing interactive visualizations. While the nature of both these methods are different, they are conceptually similar. Based on our review, we conclude that conventional statistics and machine learning are best to be integrated to develop automated data analysis tools. We also strongly believe that machine learning could be explored by health researchers to enhance conventional statistics in decision making for added reliable validation measures.

Original languageEnglish
Article number2526
Number of pages25
JournalDiagnostics
Volume12
Issue number10
DOIs
Publication statusPublished - Oct 2022

Keywords

  • comparison
  • conventional statistics
  • data analytics
  • health research
  • integration
  • machine learning

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