https://www.mdu.se/

mdu.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Artificial intelligence, machine learning and reasoning in health informatics—an overview
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0003-3802-4721
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-7305-7169
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.ORCID iD: 0000-0002-1212-7637
2021 (English)In: Intelligent Systems Reference Library, Vol. 192, Springer Science and Business Media Deutschland GmbH , 2021, p. 171-192Chapter in book (Refereed)
Abstract [en]

As humans are intelligent, to mimic or models of human certain intelligent behavior to a computer or a machine is called Artificial Intelligence (AI). Learning is one of the activities by a human that helps to gain knowledge or skills by studying, practising, being taught, or experiencing something. Machine Learning (ML) is a field of AI that mimics human learning behavior by constructing a set of algorithms that can learn from data, i.e. it is a field of study that gives computers the ability to learn without being explicitly programmed. The reasoning is a set of processes that enable humans to provide a basis for judgment, making decisions, and prediction. Machine Reasoning (MR), is a part of AI evolution towards human-level intelligence or the ability to apply prior knowledge to new situations with adaptation and changes. This book chapter presents some AI, ML and MR techniques and approached those are widely used in health informatics domains. Here, the overview of each technique is discussed to show how they can be applied in the development of a decision support system.

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2021. p. 171-192
Keywords [en]
Artificial Intelligence, Artificial Neural Network (ANN), Case-Based Reasoning (CBR), Fuzzy C-means (FCM) Clustering, Fuzzy logic, K-means clustering, K-Nearest Neighbor (k-NN), Machine learning, Random Forest (RF), Reasoning, Support Vector Machine (SVM)
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-51880DOI: 10.1007/978-3-030-54932-9_7Scopus ID: 2-s2.0-85092351054OAI: oai:DiVA.org:mdh-51880DiVA, id: diva2:1479849
Available from: 2020-10-27 Created: 2020-10-27 Last updated: 2020-10-27Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Ahmed, Mobyen UddinBarua, ShaibalBegum, Shahina

Search in DiVA

By author/editor
Ahmed, Mobyen UddinBarua, ShaibalBegum, Shahina
By organisation
Embedded Systems
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 245 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf