Classification Accuracy of Support Vector Machine, Decision Tree and Random Forest Modules when Applied to a Health Monitoring with Flexible Sensors

• Published in: Sensors & transducers Vol. 245; no. 6; pp. 83 - 89
• Main Authors: Uchida, Yasutaka, Funayama, Tomoko, Kogure, Yoshiaki
• Format: Journal Article
• Language: English
• Published: Toronto IFSA Publishing, S.L 01.10.2020
• Subjects: Accuracy; Datasets; Decision trees; Evaluation; Feature extraction; Flexible components; Machine learning; Methods; Monitoring systems; Multilayer perceptrons; Older people; People with disabilities; Regression analysis; Sensors; Support vector machines; Values

In this study, distinct machine learning-based methods of analysis were used to evaluate the most effective feature combination for a previously proposed system which detects changes in the physical conditions of human beings through eight sensors placed on various locations of a bed and floor near...