Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document

Frontiers in Public Health

article

Iqra Khurshid¹  Salma Imtiaz¹  Wadii Boulila²  Zahid Khan²  Almas Abbasi¹  Abdul Rehman Javed³  Zunera Jalil³ 

[1] Department of Software Engineering, International Islamic University;Robotics and Internet-of-Things Laboratory, Prince Sultan University;Department of Cyber Security, Air University

关键词: non-functional requirements;    healthcare;    classification;    machine learning;    requirement document;   

DOI  :  10.3389/fpubh.2022.860536

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

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 Requirement Engineering (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 (LR), Support Vector Machine (SVM), Multinomial Naive Bayes (MNB), K-Nearest Neighbors (KNN), ensemble, Random Forest (RF), and hybrid KNN rule-based machine learning (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 documents, but it also proposes a novel hybrid KNN-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.

【 授权许可】

CC BY