A Comprehensive Review on Machine Learning in Healthcare Industry: Classification, Restrictions, Opportunities and Challenges

Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Med...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 23; H. 9; S. 4178
Hauptverfasser: An, Qi, Rahman, Saifur, Zhou, Jingwen, Kang, James Jin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 22.04.2023
MDPI
Schlagworte:
Accuracy
Algorithms
Analysis
Artificial Intelligence
Classification
Clinical decision making
Computational linguistics
Data analysis
Data mining
Datasets
Discriminant analysis
Disease
Efficiency
Electronic health records
Epidemiology
Health care industry
Health care reform
Health Care Sector
healthcare
Language processing
Machine Learning
machine learning algorithms
Medical advice systems
Medical care
Medical diagnosis
Medical research
Medicine, Experimental
mobile health
Natural language interfaces
Neural networks
Pattern recognition
Public health
Quality management
Review
supervised learning
Telemedicine
Unsupervised Machine Learning
Germany
unsupervised machine learning
mobile health
machine learning
machine learning algorithms
supervised learning
healthcare
ISSN:1424-8220, 1424-8220
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Recently, various sophisticated methods, including machine learning and artificial intelligence, have been employed to examine health-related data. Medical professionals are acquiring enhanced diagnostic and treatment abilities by utilizing machine learning applications in the healthcare domain. Medical data have been used by many researchers to detect diseases and identify patterns. In the current literature, there are very few studies that address machine learning algorithms to improve healthcare data accuracy and efficiency. We examined the effectiveness of machine learning algorithms in improving time series healthcare metrics for heart rate data transmission (accuracy and efficiency). In this paper, we reviewed several machine learning algorithms in healthcare applications. After a comprehensive overview and investigation of supervised and unsupervised machine learning algorithms, we also demonstrated time series tasks based on past values (along with reviewing their feasibility for both small and large datasets).
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s23094178