Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document - Khurshid, Iqra; Imtiaz, Salma; Boulila, Wadii; Khan, Zahid; Abbasi, Almas; Javed, Abdul Rehman; Jalil, Zunera (Frontiers in Public Health, 2022)
Internet of Things (IoT) involves a set of devices that aids in achieving a smart environment. Healthcare systems, which are IoT-oriented, provide monitoring services of patients' data and help take immediate steps in an emergency. Currently, machine learning-based techniques are adopted to ensure security and other non-functional requirements in smart health care systems. However, no attention is given to classifying the non-functional requirements from requirement documents. The manual process of classifying the non-functional requirements from documents is erroneous and laborious. Missing non-functional requirements in the RE phase results in IoT oriented healthcare system with compromised security and performance. In this research, an experiment is performed where non-functional requirements are classified from the IoT-oriented healthcare system's requirement document. The machine learning algorithms considered for classification are Logistic Regression, Support Vector Machine, Multinomial Naive Bayes, K-Nearest Neighbors, ensemble, Random Forest, and hybrid K-Nearest Neighbor (KNN) rule-based ML algorithms. The results show that our novel Hybrid KNN rule-based machine learning algorithm outperforms others by showing an average classification accuracy of 75.9% in classifying non-functional requirements from IoT-oriented healthcare requirement documents. This research is not only novel in its concept of using a machine learning approach for classification of non-functional requirements from IoT-oriented healthcare system requirement document, but it also proposes a novel Hybrid K Nearest Neighbor-Rule Based machine learning algorithm for classification with better accuracy. A new dataset is also created for classification purposes, comprising requirements related to IoT-oriented healthcare systems. However, since this dataset is small and consists of only 104 requirements, this might affect the generalizability of the results of this research.