Theory and practice of integrating machine learning and conventional statistics in medical data analysis
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 s...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | Article |
Published: |
MDPI
2022
|
Subjects: | |
Online Access: | http://eprints.um.edu.my/40842/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.um.eprints.40842 |
---|---|
record_format |
eprints |
spelling |
my.um.eprints.408422023-09-26T07:07:48Z http://eprints.um.edu.my/40842/ Theory and practice of integrating machine learning and conventional statistics in medical data analysis Dhillon, Sarinder Kaur Ganggayah, Mogana Darshini Sinnadurai, Siamala Lio, Pietro Taib, Nur Aishah R Medicine 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. MDPI 2022-10 Article PeerReviewed Dhillon, Sarinder Kaur and Ganggayah, Mogana Darshini and Sinnadurai, Siamala and Lio, Pietro and Taib, Nur Aishah (2022) Theory and practice of integrating machine learning and conventional statistics in medical data analysis. Diagnostics, 12 (10). ISSN 2075-4418, DOI https://doi.org/10.3390/diagnostics12102526 <https://doi.org/10.3390/diagnostics12102526>. 10.3390/diagnostics12102526 |
institution |
Universiti Malaya |
building |
UM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
Universiti Malaya |
content_source |
UM Research Repository |
url_provider |
http://eprints.um.edu.my/ |
topic |
R Medicine |
spellingShingle |
R Medicine Dhillon, Sarinder Kaur Ganggayah, Mogana Darshini Sinnadurai, Siamala Lio, Pietro Taib, Nur Aishah Theory and practice of integrating machine learning and conventional statistics in medical data analysis |
description |
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. |
format |
Article |
author |
Dhillon, Sarinder Kaur Ganggayah, Mogana Darshini Sinnadurai, Siamala Lio, Pietro Taib, Nur Aishah |
author_facet |
Dhillon, Sarinder Kaur Ganggayah, Mogana Darshini Sinnadurai, Siamala Lio, Pietro Taib, Nur Aishah |
author_sort |
Dhillon, Sarinder Kaur |
title |
Theory and practice of integrating machine learning and conventional statistics in medical data analysis |
title_short |
Theory and practice of integrating machine learning and conventional statistics in medical data analysis |
title_full |
Theory and practice of integrating machine learning and conventional statistics in medical data analysis |
title_fullStr |
Theory and practice of integrating machine learning and conventional statistics in medical data analysis |
title_full_unstemmed |
Theory and practice of integrating machine learning and conventional statistics in medical data analysis |
title_sort |
theory and practice of integrating machine learning and conventional statistics in medical data analysis |
publisher |
MDPI |
publishDate |
2022 |
url |
http://eprints.um.edu.my/40842/ |
_version_ |
1781704532486520832 |
score |
13.160551 |