Naïve bayes on diagnostic expert system for menstrual disorders
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Abstract
Menstrual disorders often occur in women in their active reproductive period. This disorder is caused by various factors such as hormonal, ovarian, hypothalamus, and other factors. Thus, it can be stated that the causes of menstrual disorders are very broad and varied. Lack of public knowledge and awareness about women's reproductive health can have serious consequences for sufferers, such as difficulty getting pregnant, infertility, tumors, and even cancer. To be able to help people with menstrual disorders quickly and efficiently, an expert system is needed to make an initial diagnosis of menstrual disorders. In addition to helping the community, expert systems can assist experts or medical personnel in determining the initial diagnosis/anamnesis so that the evaluation of abnormal uterine bleeding can result in appropriate treatment. In this study, researchers built an expert system with the Naïve Bayes web-based method to get an initial diagnosis in the form of a percentage of possible diseases suffered by users based on the selected symptoms. By testing the system, it can be concluded that the system built by applying the Naïve Bayes method can accurately diagnose types of menstrual disorders with a percentage of 84% based on data and symptoms experienced by patients. Based on other tests, the system functions as it should, and the community considers the system acceptable, good, and proper.
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