Identification and Interconnection of Symptoms of Hypertension using Interpretive Structural Model: A Qualitative Survey

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Hypertension (HTN) is one of the major global public health maladies. Equally, the impact on the incidence of hypertension in smart cities is increasing due to the abundant use of electromagnetic fields like 5G. HTN may not have any warning indications so the interconnection of its symptoms is crucial for early diagnosis and management. Thus, in order to examine a set of symptoms and how they relate to one another in HTN, the authors employed interpretive structural model (ISM). In the first stage, the authors identified a total of 18 symptoms of hypertension by review. After an interview with the expert panel, 17 additional symptoms were found in the second stage. In the third stage, expert panel members were asked to rate the symptoms with a score 1 to 4. The authors used an ISM in the fourth stage to develop a causality rulebase for the diagnosis of hypertension. Any combination of symptoms, such as 1. Dizziness followed by a) Chest pain + Palpitation + Transient chest pain after exertion /or, b) Headache + Fainting. 2. Headache followed by a) Chest pain + Palpitation + Transient chest pain after exertion /or, b) Dizziness + Fainting. 3. Fainting followed by a) Chest pain + Palpitation + Transient chest pain after exertion, /or, b) Dizziness + Headache, may be used to identify hypertension. It was discovered that the presence of nosebleed symptoms did not contribute to the hypertension diagnosis. Data analytics is a common tool used by smart cities to enhance healthcare facilities. By contributing insights into the early detection of hypertension throughout smart cities, the ISM model can support data-driven decision-making and enhance the healthcare system.