A Fuzzy Intelligent Framework for Healthcare Diagnosis and Monitoring of Pregnancy Risk Factor in Women

Uduak Umoh, Emmanuel Nyoho


The harmful effect of pregnancy risk factors to the body cannot be underestimated. Pregnancy risk factors are all the aspects that endanger the life of the mother and the baby. The infant mortality rates are still high in developing countries despite national and international efforts to redress this problem of pregnancy risk factors. The operations of the prediction of pregnancy risk factors are complex and risky due to fluctuation in the diagnosis of these risk factors. This is due to the vagueness, incompleteness, and uncertainty of the information used. Also, the health population index, which is based primarily on the result of medical research, has a strong impact upon all human activities. Medical experts are considered best fit for interpretation of data and setting the diagnosis, but medical decision making becomes a very hard activity because the human experts, who have to make decision, can hardly process the huge amount of data. This paper presents a fuzzy logic model for the diagnosis and monitoring of pregnancy risk factor for in order to make accurate reasoning with huge amount of uncertain knowledge. The model is developed based on clinical observations, medical diagnosis and the expert’s knowledge. Twenty-five pregnant patients are selected and studied and the observed results computed in the range of predefined limit by the domain experts. The model will provide decision support platform to pregnancy risk factor researchers, physicians and other healthcare practitioners in obstetrical. The study will also guide healthcare practitioners in obstetrical and gynecology clinic regions in educating the women more about the pregnancy risk factors and encouraged them to start antenatal clinic early in pregnancy.

Keyword: Fuzzy inference System, Artificial Intelligence, Expert System, Pregnancy risk factors, Infant mortality, Pregnancy outcome

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