Expert System to Diagnose Ovarial Cyst Disease using Web-Based Bayes Method

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Khairunisa
R. Mahdalena Simanjorang

Abstract

Ovarian cyst disease is a disease that is often experienced by a woman, this disease is very complicated, subtle and unique, because this disease is similar to pregnancy and maybe all women have a risk of getting this disease. But on the other hand the lack of attention from the public to this ovarian cyst disease and the lack of knowledge about the early symptoms of ovarian cysts causes the general public to be susceptible to the disease. Lack of experts to handle ovarian cyst disease at health centers, especially health centers located far from the city. This of course has an impact on the delay in handling patients with ovarian cysts. To overcome this problem, appropriate action is needed to diagnose the ovarian cyst disease. An expert system using the Bayes method for diagnosing ovarian cysts is the best solution for recognizing the symptoms of ovarian cysts as early as possible, knowing the cause of the disease and how to control it. In making this system an expert in the field of ovarian cyst disease is needed to obtain accurate data regarding information on ovarian cyst disease. This expert system for diagnosing ovarian cysts is designed using a web-based PHP programming language. The design of the knowledge base in this system is made dynamically to make it easier to manage data such as adding, changing and deleting data.

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How to Cite
Khairunisa, & Simanjorang, R. M. (2021). Expert System to Diagnose Ovarial Cyst Disease using Web-Based Bayes Method. Login : Jurnal Teknologi Komputer, 15(2), 67-71. Retrieved from http://login.seaninstitute.org/index.php/Login/article/view/88

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