ArticleActa informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH2018
Design a Fuzzy Rule-based Expert System to Aid Earlier Diagnosis of Gastric Cancer.
Article in Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Use of Clinical Decision Support Systems for Diagnosis and Prognosis of Gastric Cancer: A Scoping Review.Health science reports · 2025Review
- A Neoteric Feature Extraction Technique to Predict the Survival of Gastric Cancer Patients.Diagnostics (Basel, Switzerland) · 2024Article
- Using an adaptive network-based fuzzy inference system for prediction of successful aging: a comparison with common machine learning algorithms.BMC medical informatics and decision making · 2023Article
- Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors.BMC gastroenterology · 2023Article
- Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer.Medical journal of the Islamic Republic of Iran · 2021Article
- A Framework for Detecting and Analyzing Behavior Changes of Elderly People over Time Using Learning Techniques.Sensors (Basel, Switzerland) · 2020Article
- Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey.Yearbook of medical informatics · 2019Review
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionScreening and health check-up programs are most important sanitary priorities, that should be undertaken to control dangerous diseases such as gastric cancer that affected by different factors. More than 50% of gastric cancer diagnoses are made during the advanced stage. Currently, there is no systematic approach for early diagnosis of gastric cancer.
objectiveto develop a fuzzy expert system that can identify gastric cancer risk levels in individuals.
methodsThis system was implemented in MATLAB software, Mamdani inference technique applied to simulate reasoning of experts in the field, a total of 67 fuzzy rules extracted as a rule-base based on medical expert's opinion.
results50 case scenarios were used to evaluate the system, the information of case reports is given to the system to find risk level of each case report then obtained results were compared with expert's diagnosis. Results revealed that sensitivity was 92.1% and the specificity was 83.1%.
conclusionsThe results show that is possible to develop a system that can identify High risk individuals for gastric cancer. The system can lead to earlier diagnosis, this may facilitate early treatment and reduce gastric cancer mortality rate.
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