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

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 9; p. 4178
• Main Authors: An, Qi, Rahman, Saifur, Zhou, Jingwen, Kang, James Jin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.04.2023; MDPI
• Subjects: 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
• DOI: 10.3390/s23094178

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).