Frame-based System for Diagnosing Infertility in Males and Females

Diagnosis plays a crucial role in saving the life of a patient. However, due to the challenges faced by medical practitioners such as; few available resources, little amount of time dedicated to diagnose each patient, few numbers of specialists, emergence of new diseases and similarities of symptoms of diseases may hinder achieving accurate diagnosis. Infertility may be caused by a range of medical condition and abnormalities such as diseases, infections and hormonal imbalances in the reproductive system. The prevalence of infertility has negatively affected many couples globally especially in Africa where it is often linked with different traditional superstition in some societies. This led to the need for the development of systems capable of predicting and diagnosing diseases. In this research work, the expert System developed employs the frame-based approach to assess and predict the possible infertility problem that a patient may have based on the symptoms and patient information provided into the system. Outcomes of diagnosis presented to users solely depend on reasoning method implemented in the knowledge base of the system. The system showed an excellent predictive ability of 98% when scoring based on accuracy. It was evaluated on fifty (50) randomly selected infertility cases from the case file of patients. The system was able to effectively predict forty nine (49) infertility cases correctly and one (1) incorrectly. From the study, it is concluded that the frame-based system will assist not only medical practitioners but also individuals affected in achieving timely diagnosis since it can be accessed remotely. Furthermore, the system has the ability to store health records, diagnosis and generate statistical reports of patients.