Expert System for Diagnosing Cancer Using Bayes Theorem Method

  • Indri Aurellia Apsari Sembiring STMIK Kaputama, Binjai, Sumatera Utara
  • Relita Buaton STMIK Kaputama, Binjai, Sumatera Utara
  • Husnul Khair STMIK Kaputama, Binjai, Sumatera Utara

Abstract

Cancer is a dangerous disease that is not contagious and is caused by abnormal cells or tissues that can grow rapidly. The rapid development of this network will cause several complications of this disease which develop rapidly as well. Adam Malik General Hospital is one of the government hospitals that has complete facilities to treat cancer patients. Because it is a regionally centralized general hospital, each patient has quite a long queue when conducting consultations and treatment, this can result in physical fatigue in patients because it takes too long in the queue. To overcome these problems, we need a system that can provide information and detect early cancer diagnoses. So that the doctor can use the results of the diagnosis issued by the system so that it can be analyzed further. This of course can speed up the time the patient is in the examination, so that the treatment process can run effectively and efficiently. An expert system is a system that can store information from an expert, such as a doctor, for example. One of the methods that can be used is the Bayes Theorem method, which is a theory of probable conditions that calculates the probability of an event that depends on evidence. From the results of the trials that have been carried out, the results show that the patient suffers from cancer, namely breast cancer with a value of 81.60%.

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Published
2024-01-23
How to Cite
SEMBIRING, Indri Aurellia Apsari; BUATON, Relita; KHAIR, Husnul. Expert System for Diagnosing Cancer Using Bayes Theorem Method. International Journal of Informatics, Economics, Management and Science (IJIEMS), [S.l.], v. 3, n. 1, p. 84-92, jan. 2024. ISSN 2809-8471. Available at: <http://journal.stmikjayakarta.ac.id/index.php/ijiems/article/view/1231>. Date accessed: 14 apr. 2024. doi: https://doi.org/10.52362/ijiems.v3i1.1231.

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