The International Arab Journal of Information Technology (IAJIT)


An Architecture of IoT-Aware Healthcare Smart System by Leveraging Machine Learning

In a healthcare environment, Internet of Things (IoT) sensors’ devices are integrated to help patients and Physicians remotely. Physicians interconnect with their patients to monitor their current health situation. However, a considerable number of real-time patient data produced by IoT devices makes healthcare data intensive. It is challenging to mine valuable features from real-time data traffic for efficient recommendations to patients. Thus, an intelligent healthcare system must analyze the real-time health conditions and predict suitable drugs based on the diseases’ symptoms. In this paper, an IoT architectural model for smart health care is proposed. This model utilizes clustering and Machine Learning (ML) techniques to predict suitable drugs for patients. First, Spark is used to manage the collected data on distributed servers. Second, the K-means clustering algorithm is used for disease-based categorization to make groups of the related features. Third, predictor techniques, i.e., Naïve Bayes and random forest, are used to classify suitable drugs for the patients. Two standard Unique Client Identifier (UCI) machine learning datasets have been conducted in the experiments. The first dataset consists of different types of thyroid diseases, while the second dataset contains drugs with recommended medicines. The experimental results depict that the performance, i.e., the accuracy of the proposed model, is superior in predicting the suitable drugs for patients, by which it provides a highly effective delivery healthcare service in IoT. Random Forest correctly classified 99.23% instances while Naive Bayes results are 95.52%.


[1] Ambarkar S. and Shekokar N., Internet of Things, Smart Computing and Technology: A Roadmap Ahead, Springer, 2020.

[2] Amendola S., Lodato R., Manzari S., Occhiuzzi C., and Marrocco G., “RFID Technology for IoT- Based Personal Healthcare in Smart Spaces,” IEEE Internet of Things Journal, vol. 1, no. 2, pp. 144-152, 2014.

[3] Callahan A. and Shah N., “Machine Learning in Healthcare,” Key Advances in Clinical Informatics, pp. 279-291, 2018.

[4] Catarinucci L., De Donno D., Mainetti L., Palano l., Patrono l., Stefanizzi M., and Tarricone L., “An IoT-Aware Architecture for Smart Healthcare Systems,” IEEE Internet of Things Journal, vol. 2, no. 6, pp. 515-526, 2015.

[5] Chen M., Hao Y., Hwang K., Wang L., and Wang L., “Disease Prediction By Machine Learning Over Big Data from Healthcare Communities,” IEEE Access, vol. 5, pp. 8869- 8879, 2017.

[6] Chi Z., Li Y., Sun H., Yao Y., and Zhu T., “Concurrent Cross-Technology Communication Among Heterogeneous IoT Devices,” IEEE/ACM Transactions on Networking, vol. 27, no. 3, pp. 932-947, 2019.

[7] Da-Xu L., He W., and Li S., “Internet of Things in Industries: A Survey,” IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233- 2243, 2014.

[8] Darshan K. and Anandakumar K., “A Comprehensive Review on Usage of Internet of Things (Iot) in Healthcare System,” in Proceedings of International Conference on Emerging Research in Electronics, Computer Science and Technology, Mandya, pp. 132-136, 2015.

[9] Darwish A., Hassanien A., Elhoseny A., Sangaiah A., and Muhammad K., “The Impact of The Hybrid Platform of Internet of Things and Cloud Computing on Healthcare Systems: Opportunities, Challenges, and Open Problems,” Journal of Ambient Intelligence and Humanized Computing, pp. 1-16, 2017. 170 The International Arab Journal of Information Technology, Vol. 19, No. 2, March 2022

[10] Dehury C. and Sahoo P., “Design and Implementation of A Novel Service Management Framework for Iot Devices in Cloud,” Journal of Systems and Software, vol. 119, pp. 149-161, 2016.

[11] Eiras-Franco C., Bolón-Canedo V., Ramos S., González-Domínguez J., Alonso-Betanzos A., and Tourino J., “Multithreaded and Spark Parallelization of Feature Selection Filters,” Journal of Computational Science, vol. 17, pp. 609-619, 2016.

[12] Esposito C., Castiglione A., Tudorica C., and Pop F., “Security and Privacy for Cloud-Based Data Management in the Health Network Service Chain: A Microservice Approach,” IEEE Communications Magazine, vol. 55, no. 9, pp. 102-108, 2017.

[13] Gerla M., Lee E., Pau G., and Lee U., “Internet of Vehicles: from Intelligent Grid to Autonomous Cars and Vehicular Clouds,” in Proceedings of IEEE World Forum on Internet of Things, Seoul, pp. 241-246, 2014.

[14] Guerrero-Ibanez J., Zeadally S., and Contreras- Castillo J., “Integration Challenges of Intelligent Transportation Systems with Connected Vehicle, Cloud Computing, and Internet of Things Technologies,” IEEE Wireless Communications, vol. 22, no. 6, pp. 122-128, 2015.

[15] Hakimi A., Hassan N., Anwar K., Zakaria A., and Ashraf A., “Development of Real-Time Patient Health (Jaundice) Monitoring Using Wireless Sensor Network,” in Proceedings of 3rd International Conference on Electronic Design, Phuket, pp. 404-409, 2016.

[16] Khatri I., Farooq S., and Khatri M., “Preventing Stroke At Door Step-Need for A Paradigm Shift in Delivery of Preventive Healthcare,” Pakistan Journal of Neurological Sciences, vol. 11, no. 2, pp. 1-4, 2016.

[17] Knublauch H., Fergerson R., Noy N., and Musen M., “The Protégé OWL plugin: An open Development Environment for Semantic Web Applications,” in Proceedings of 3rd International Semantic Web Conference, Hiroshima, pp. 229- 243, 2004.

[18] Koliopoulos A., Yiapanis P., Tekiner F., Nenadic G., and Keane J., “A Parallel Distributed Weka Framework for Big Data Mining Using Spark,” in Proceedings of IEEE International Congress on Big Data, New York City, pp. 9-16, 2015.

[19] Kumar P. and Gandhi U., “A Novel Three-Tier Internet of Things Architecture with Machine Learning Algorithm for Early Detection of Heart Diseases,” Computers and Electrical Engineering, vol. 65, pp. 222-235, 2018.

[20] Lane N., Bhattacharya S., Georgiev P., Forlivesi C., and Kawsar F., “An Early Resource Characterization of Deep Learning on Wearables, Smartphones and Internet-of-Things Devices,” in Proceedings of International Workshop on Internet of Things Towards Applications, Seoul, pp. 7-12, 2015.

[21] Luo X., Liu J., Zhang D., and Chang X., “A Large-Scale Web Qos Prediction Scheme for The Industrial Internet of Things Based on A Kernel Machine Learning Algorithm,” Computer Networks, vol. 101, pp. 81-89, 2016.

[22] Maksimović M., Vujović V., and Periśić B., “A Custom Internet of Things healthcare system,” in Proceedings of 10th Iberian Conference on Information Systems and Technologies, Aveiro, pp. 1-6, 2015.

[23] Malik K. Ahmad T., Farhan M., Aslam M., Jabbar S., Khalid S., and Kim M., “Big-Data: Transformation from Heterogeneous Data to Semantically-Enriched Simplified Data,” Multimedia Tools and Applications, vol. 75, no. 20, pp. 12727-12747, 2016.

[24] Manogaran G., Lopez D., Thota C., Abbas K., Pyne S., and Sundarasekar R., Innovative Healthcare Systems for the 21st Century, Springer, 2017.

[25] Marin L., Pawlowski M., and Jara A., “Optimized ECC Implementation for Secure Communication between Heterogeneous Iot Devices,” Sensors, vol. 15, no. 9, pp. 21478- 21499, 2015.

[26] Mehmood Y., Ahmad F., Yaqoob I., Adnane A., Imran M., and Guizani S., “Internet-of-Things- Based Smart Cities: Recent Advances and Challenges,” IEEE Communications Magazine, vol. 55, no. 9, pp. 16-24, 2017.

[27] Meidan Y., Bohadana M., Shabtai A., Guarnizo J., Ochoa M., Tippenhauer N., and Elovici Y., “ProfilIoT: a Machine Learning Approach for Iot Device Identification Based on Network Traffic Analysiss,” in Proceedings of 32nd Annual ACM Symposium on Applied Computing, Marrakech Morocco, pp. 506-509, 2017.

[28] Miljkovic D., Aleksovski D., Podpečan V., Lavrač N., Malle B., and Holzinger A., Machine Learning for Health Informatics, Springer, 2016.

[29] Moosavi S., Gia T., Nigussie E., Rahmani A., Virtanena S, Tenhunena H., Isoaho J., “End-to- End Security Scheme for Mobility Enabled Healthcare Internet of Things,” Future Generation Computer Systems, vol. 64, pp. 108- 124, 2016.

[30] Pang Z., Chen Q., Han W., and Zheng L., “Value-Centric Design of The Internet-of-Things Solution for Food Supply Chain: Value Creation, Sensor Portfolio and Information Fusion,” Information Systems Frontiers, vol. 17, no. 2, pp. 289-319, 2015.

[31] Paul P. and Saraswathi R., “The Internet of Things-A Comprehensive Survey,” in An Architecture of IoT-Aware Healthcare Smart System by Leveraging Machine Learning 171 Proceedings of International Conference on Computation of Power, Energy Information and Commuincation, Melmaruvathur, pp. 421-426, 2017.

[32] Perera C., Zaslavsky A., Christen P., and Georgakopoulos D., “Context Aware Computing for The Internet of Things: A Survey,” IEEE Communications Surveys and Tutorials, vol. 16, no. 1, pp. 414-454, 2014.

[33] Quinlan R., “Thyroid Disease Data Set,” 1987.

[34] Rolim C., Koch F., Westphall C., Werner J., Fracalossi A., and Salvador G., “A Cloud Computing Solution for Patient's Data Collection in Health Care Institutions,” in Proceedings of 2nd International Conference on Ehealth, Telemedicine, and Social Medicine, Saint Maarten, pp. 95-99, 2010.

[35] Santamaria A., De Rango F., Serianni A., and Raimondo P., “A Real Iot Device Deployment for E-Health Applications Under Lightweight Communication Protocols, Activity Classifier and Edge Data Filtering,” Computer Communications, vol. 128, pp. 60-73, 2018.

[36] Shafiq M., Ji L., Liu A., Pang J., and Wang J., “A First Look at Cellular Machine-to-Machine Traffic: Large Scale Measurement and Characterization,” ACM SIGMETRICS Performance Evaluation Review, vol. 40, no. 1, pp. 65-76, 2012.

[37] Shah J. and Bhat H., Internet of Things Use Cases for the Healthcare Industry, Springer, 2020.

[38] Shrouf F. and Miragliotta G., “Energy Management Based on Internet of Things: Practices and Framework for Adoption in Production Management,” Journal of Cleaner Production, vol. 100, pp. 235-246, 2015.

[39] Taiwo O. and Ezugwu A., “Smart Healthcare Support for Remote Patient Monitoring During Covid-19 Quarantine,” Informatics in Medicine Unlocked, vol. 20, pp. 100428, 2020.

[40] Tissaoui A. and Saidi M., “Uncertainty in IoT for Smart Healthcare: Challenges, and Opportunities,” in Proceedings of International Conference on Smart Homes and Health Telematics, Hammamet, pp. 232-239, 2020.

[41] Ullah F., Habib M., Farhan M., Khalid S., Durrani M., and Jabbar S., “Semantic Interoperability for Big-Data in Heterogeneous Iot Infrastructure for Healthcare,” Sustainable Cities and Society, vol. 34, pp. 90-96, 2017.

[42] Ventura D., Casado-Mansilla D., López-de- Armentia J., Garaizar P., López-de-Ipina D., and Catania V., “ARIIMA: A Real IoT Implementation of A Machine-Learning Architecture for Reducing Energy Consumption,” in Proceedings of International Conference on Ubiquitous Computing and Ambient Intelligence, Belfast, pp. 444-451, 2014.

[43] Whitmore A., Agarwal A., and Da Xu L., “The Internet of Things-A Survey of Topics and Trends,” Information Systems Frontiers, vol. 17, no. 2, pp. 261-274, 2015.

[44] Wu T., Wu F., Redouté J., and Yuce M., “An Autonomous Wireless Body Area Network Implementation Towards IoT Connected Healthcare Applications,” IEEE Access, vol. 5, pp. 11413-11422, 2017.

[45] Xiao G., Guo J., Xu L., and Gong Z., “User interoperability with Heterogeneous Iot Devices Through Transformation,” IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1486- 1496, 2014.

[46] Zaharia M. Xin R., Wendell P., Das T., Armbrust M., Dave A., Meng X., Rosen J., Venkataraman S., Franklin M., Ghodsi A., Gonzalez J., Shenker S., and Stoica I., “Apache Spark: A Unified Engine for Big Data Processing,” Communications of the ACM, vol. 59, no. 11, pp. 56-65, 2016.

[47] Zanella A., Bui N., Castellani A., Vangelista L., and Zorzi M., “Internet of Things for Smart Cities,” IEEE Internet of Things Journal, vol. 1, no. 1, pp. 22-32, 2014.

[48] Zhong H., Zhou Y., Zhang Q., Xu Y., and Cui J., “An Efficient and Outsourcing-Supported Attribute-Based Access Control Scheme for Edge-Enabled Smart Healthcare,” Future Generation Computer Systems, vol. 115, pp. 486- 496, 2021. 172 The International Arab Journal of Information Technology, Vol. 19, No. 2, March 2022 Hamza Aldabbas received his PhD Degree in Computer Science and Software Engineering, De Montfort University, Leicester-United Kingdom (2009-2012). Previously M.Sc, Computer Science (2009) and B.Sc Computer Information Systems (2006) from Al-Balqa Applied University, Al-Salt, Hashemite Kingdom of Jordan. He is currently an Associate Professor at Al-Balqa Applied University /Prince Abdullah bin Ghazi Faculty of Information and Communication Technology-Jordan 2013 until now). Previously a lecturer at De Montfort University/United Kingdom with responsibility for teaching and project supervision at B.Sc&M.Sc levels (2010-to 2012). His research interests include security, IoT, ad hoc networks, machine learning and natural language processing. Dheeb Albashish received a Ph.D. degree for his work in thearea of ensemble and features selection for medical images in2017 at the UKM, Malaysia. He is currently an Assistant Professor in the Department of Computer Science, Al-Balqa Applied University, Jordan. His main current areas are image processing, feature selection, deep learning and IoT. Khalaf Khatatneh currently works As Dean of the Prince Abdullah bin Ghazi faculty for Communication and Information Technology also Dr.Khatatneh working as Computer Center Manager attached with Huawei Academy supervisor at Al- Balqa Applied University. Khatatneh does research in Databases and Artificial Intelligence. Rashid Amin Is working as Lecturer at the Department of Computer Science, University of Engineering and Technology, Taxila, Pakistan since august, 2014. Before this, he worked as Lecturer at the University of Wah, Wah Cantt, Pakistan for 4 years. He received MS Computer Science and Master of Computer Science (MCS) from International Islamic University, Islamabad. His MS thesis was on Peer -to -Peer Overlay Network over Mobile Ad hoc Network. He is a Ph.D. student at Comsats Institute of Information Technology, Wah Cantt. He has completed his thesis that is under evaluation. His area of research is Hybrid Software Defined Networking. His current research interests include SDN, HSDN, Distributed Systems, P2P and Network Security. He has published several research papers on the topics of hybrid SDN, SDN in well reputed venues (like IEEE Communication Surveys & Tutorial, IEEE Access, Electronics MDPI, IJACSA, etc.). He has been serving as reviewer for international Journals (e.g., NetSoft, LCN, GlobeCom, Fit, IEEE Wireless Communication, IEE IoT, IEEE J - SAC, IEEE Access, IEEE System Journal, , Pervasive and Mobile Computing (PMC), Journal of Network and Computer Applications (JNCA), Peer -to Peer Networking and Applications (PPNA), and the Frontiers of Computer Science (FCS), International Journal of Communication System, etc.