International Journal of Electrical and Computer Engineering (IJECE) Vol. 12, No. 3, June 2022, pp. 3166~3175 ISSN: 2088-8708, DOI: 10.11591/ijece.v12i3.pp3166-3175  3166 Journal homepage: http://ijece.iaescore.com Review on hypertension diagnosis using expert system and wearable devices Muhammad Izzuddin Mohd Sani 1 , Nur Atiqah Sia Abdullah 2,3 , Marshima Mohd Rosli 2,3 1 Department Supervisory Control and Data Acquisition (SCADA), Willowglen (Malaysia) Sdn Bhd, Kuala Lumpur, Malaysia 2 Center of Studies for Computer Science, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia 3 Knowledge and Software Engineering Research Group, Research Nexus UiTM (ReNeU) Office of Deputy Vice Chancellor (Research and Innovation), Universiti Teknologi MARA, Shah Alam, Malaysia Article Info ABSTRACT Article history: Received Mar 16, 2021 Revised Oct 10, 2021 Accepted Nov 15, 2021 The popularity of smartphones and wearable devices is increasing in the global market. These devices track physical exercise records, heartbeat, medicines, and self-health diagnosis. The wearable devices can also collect personal health parameters include hypertension diagnosis. Hypertension is one of the risk factors for cardiovascular-related diseases among the Malaysian population. Many mobile applications are paired with wearable devices to monitor health conditions, but none of them able to diagnose hypertension. In this study, we reviewed research papers that focused on hypertension using expert systems and wearable devices. We performed a systematic literature review based on hypertension factors, expert systems, and wearable devices. We found 15 specific research papers after the filtering process. The key findings highlighted three main focuses, which are the factors of hypertension, the expert system techniques, and the types of sensors in wearable devices. Blood pressure is the most common factor of hypertension that can be collected by wearable devices. As for the expert system techniques, we determined the three most common techniques are machine learning, neural network, and fuzzy logic. Lastly, the wrist band is the most common sensor for wearable devices in hypertension-related research. Keywords: Expert system Hypertension management Machine learning Systematic review Wearable devices This is an open access article under the CC BY-SA license. Corresponding Author: Nur Atiqah Sia Abdullah Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA 40450 Shah Alam, Selangor, Malaysia Email: atiqah684@uitm.edu.my 1. INTRODUCTION The technology of mobile phones becomes trendy and receives high demand since the introduction of smartphones to consumers [1]. The challenges increase when smartphones start to pair with wearable devices like a smart watch and wireless earphones. Moreover, wearable devices have become trending among smartphone users with many other applications [2]–[4]. The popularity of wearable devices is increasing as there are currently used to keep a healthier lifestyle like losing weight and getting a preventive measure for having disease due to inactive physical activities. The rise of the health care cost and aging society in some countries had also contributed to this popularity [5], [6]. As in addition, the fact of active physical activities can lower the risk of suffering for over 20 health conditions [7]. The popularity of wearable devices in Malaysia also seems to follow global trends [8]. The global market of wearable devices will be double by 2021 compared to 2017 statistics [9]. The wearable devices can provide preventive measures, which able to check and monitor the health condition of the users