Development of Rule Based Expert System for the Diagnosis of Maternal Complications during Pregnancy

Abstract

Maternal complications are morbidities that occur during pregnancy which include gestational diabetes mellitus, maternal sepsis, sexually transmitted diseases, obesity, anemia, urinary tract infections, hypertension, and heart disease. Some signs of pregnancy are similar to the signs and symptoms of pregnancy complications such as sepsis and gestational diabetes making them difficult to diagnose. Due to this, maternal disease diagnostic error is becoming a common life threatening condition and the cause of maternal mortality, especially in resource-limited areas. Due to a scarce number of health care professionals in low resource setting countries, the failure of the physician to generate a proper hypothesis from patient‟s communication, failure to order the exact physical examination and diagnostic test, and the loss of patient records maternal mortality and morbidity is still high. The study also assessed the prevalence of maternal complication diagnostic errors, the causes, and effects of diagnostic error in selected area of maternity settings. As a result the study provides information about the prevalence, causes, and effects of diagnostic error during antennal care visit in selected health facilities of Ethiopia. It also developed a rule-based expert system for the diagnosis of maternal complications during pregnancy in order to minimize the risk associated with diagnostic error. The developed system incorporates the diagnosis of the three maternal complications during pregnancy namely, preeclampsia, gestational diabetes mellitus, and maternal sepsis. The designed rule-based expert system was integrated with a web-based user interface using the Django framework in order to provide a simple and attractive user interface. The designed system is easy to use; reduces the workload of physicians, especially in developing countries like Ethiopia where there is a scarcity in a number of obstetricians. The designed expert system achieved 94% of accuracy. The study can endorse the health service given during antenatal care visits and promote the health outcome of pregnant women