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Adaptive Neuro-Fuzzy Inference Model for Monitoring Hypertension Risk

Author

Listed:
  • Ngozi Chidozie Egejuru

    (Obafemi Awolowo University, Nigeria)

  • Oluwadare Ogunlade

    (Obafemi Awolowo University, Nigeria)

  • Peter Adebayo Idowu

    (Obafemi Awolowo University, Nigeria)

  • Adanze Onyenonachi Asinobi

    (University of Ibadan, Nigeria)

Abstract

This study presented a model to classify risk of hypertension using Adaptive Neuro-Fuzzy Inference System (ANFIS). In order to develop the model cardiologists from teaching hospitals in Nigeria were interviewed so as to identify required variables for classification. Structured questionnaires were used to elicit information about the risk factors and the associated risk of hypertension from respondents. The MATLAB ANFIS Toolbox was used to simulate the model. The result of this study revealed that there were 33 main variables identified for monitoring hypertension risk and they were in line with the WHO/ISH classification standard. The result showed that majority of the patients selected had very high risk (57.0%) of hypertension which consisted more than 50% of the patients selected followed by 19% representing patients with high risk of hypertension, followed by patients with medium risk of hypertension. In conclusion, the model assist healthcare professionals to have accurate diagnosis, early detection and proper management of hypertension.

Suggested Citation

  • Ngozi Chidozie Egejuru & Oluwadare Ogunlade & Peter Adebayo Idowu & Adanze Onyenonachi Asinobi, 2021. "Adaptive Neuro-Fuzzy Inference Model for Monitoring Hypertension Risk," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 16(4), pages 1-32, October.
    • Handle: RePEc:igg:jhisi0:v:16:y:2021:i:4:p:1-32
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