Prediction of medical device performance using machine learning techniques: infant incubator case study

• Published in: Health and technology 2020, Vol.10 (1), p.151-155
• Main Authors: Kovačević, Živorad, Gurbeta Pokvić, Lejla, Spahić, Lemana, Badnjević, Almir
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Artificial intelligence; Big Data; Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Case studies; Classifiers; Computational Biology/Bioinformatics; Datasets; Decision trees; Disease prevention; Engineering; Expert systems; Failure; Health care; Infants; Inspections; Life expectancy; Machine learning; Medical devices; Medical electronics; Medical equipment; Medical technology; Medicine/Public Health; Original Paper; Patients; R & D/Technology Policy; Resource management; Support vector machines
• ISSN: 2190-7188; 2190-7196
• DOI: 10.1007/s12553-019-00386-5

With development in the area of electronics and artificial intelligence (AI), medical devices (MD) have been sophisticated as well. MD management strategies today are very different than decades ago, so it is reasonable to consider how we can prepare for where we are going in the future. This paper presents the result of application of machine learning (ML) techniques in management of infant incubators in healthcare institutions. A total of 140 samples was used for development of Expert system based on ML classifiers. These samples were collected during 2015–2017 period, as part of yearly inspections of incubators in healthcare institutions by ISO 17020 accredited laboratory. Dataset division 80–20 was used for classifiers development and validation. Performance of the following machine learning algorithms was investigated: Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), k-Nearest Neighbour (kNN), and Support Vector Machine (SVM). Resulting classifiers were compared by performance and classifier based on Decision Tree algorithm yielded highest accuracy (98.5%) among other tested systems. Obtained results suggest that by introducing ML algorithms in MD management strategies benefit healthcare institution firstly in terms of increase of safety and quality of patient diagnosis and treatments, but also in cost optimization and resource management.