EXPERT SYSTEM FOR DISEASE IDENTIFICATION BASED ON HEMATOCHEZIA SYMPTOMS WITH NAÏVE BAYES METHOD

  • Dasril Aldo (1*) Institut Teknologi Telkom Purwokerto
  • Alwendi Alwendi (2) Universitas Graha Nusantara Padangsidimpuan
  • Adanti Wido Paramadini (3) Institut Teknologi Telkom Purwokerto
  • Ilwan Syafrinal (4) Universitas Universal
  • Sapta Eka Putra (5) Universitas Tamansiswa Padang

  • (*) Corresponding Author
Keywords: expert systems, hematochezia, identification, naïve bayes

Abstract

Hematochezia is a common clinical symptom in various gastrointestinal diseases, requiring accurate diagnosis for effective treatment. This study aims to develop an expert system for the rapid and precise identification of hematochezia-causing diseases. The expert system is designed to assist patients in efficiently recognizing diseases, minimizing treatment failure risks. It employs the Naïve Bayes method, a data calculation approach involving summing combinations and frequencies of each dataset. The expert system methodology begins with training using a dataset comprising hematochezia symptoms and corresponding disease diagnoses. The dataset is input into a database as training data. Subsequently, it undergoes classification and training stages. Symptom data can then be processed using the Naïve Bayes method. The system's end result displays probability values for each disease based on provided symptoms. This analysis relies on specific symptoms selected by the user, such as Rectal Pain, Hematochezia, Constipation, Fatigue, and Abdominal Cramps. It yields a Hemorrhoids diagnosis with a posterior probability of 0.514738. In testing with 35 sample cases, the expert system exhibited a remarkable accuracy rate of 94.29%. This expert system efficiently supports disease diagnosis based on hematochezia symptoms, aiding in swift and accurate identification.

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Published
2024-02-12
How to Cite
[1]
D. Aldo, A. Alwendi, A. Paramadini, I. Syafrinal, and S. Putra, “EXPERT SYSTEM FOR DISEASE IDENTIFICATION BASED ON HEMATOCHEZIA SYMPTOMS WITH NAÏVE BAYES METHOD”, jitk, vol. 9, no. 2, pp. 247-256, Feb. 2024.
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