Classification of Non-Functional Requirements From IoT Oriented Healthcare Requirement Document
Bibliographic Data
| ID | 22087439 |
|---|---|
| Authors | Iqra Khurshid (International Islamic University, Islamabad), Salma Imtiaz (0000-0003-3689-9663, International Islamic University, Islamabad), Wadii Boulila (0000-0003-2133-0757, Prince Sultan University, corresponding author), Zahid A Khan (0000-0003-3756-1058, Prince Sultan University), Zahid Khan (0000-0002-0710-2419), Almas Abbasi (0000-0002-3449-6572, International Islamic University, Islamabad), Abdul Rehman Javed (0000-0002-0570-1813, Air University, corresponding author), Zunera Jalil (0000-0003-2531-2564, Air University) |
| Year | 2022 |
| Volume | 10 |
| Pages | 860536-860536 |
| Publication date | 2022-03-18 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.860536 |
| PMID | 35372217 |
| OpenAlex | W4220739531 |
| Language | EN |
| Citations received | 1 |
| References cited | 41 |
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
Data mining · Functional requirement · Machine learning · Naive Bayes classifier · Random forest · Software engineering · Support vector machine · Computer Science · IoT and Edge/Fog Computing · Software Engineering Techniques and Practices · Software System Performance and Reliability · Artificial Intelligence
| Unique citing works | 1 |
|---|---|
| Citations per year | 0.25 |
| Citation span | 2022 - 2022 (1) |
| Citation velocity | historical |
| Highly cited | No |
| Citation types | Neutral: 1 |