Abstract
Gastric cancer (GC), one of the most common cancers around the world, is a multifactorial disease and there are many risk factors for this disease. Assessing the risk of GC is essential for choosing an appropriate healthcare strategy. There have been very few studies conducted on the development of risk assessment systems for GC. This study is aimed at providing a medical decision support system based on soft computing using fuzzy cognitive maps (FCMs) which will help healthcare professionals to decide on an appropriate individual healthcare strategy based on the risk level of the disease. FCMs are considered as one of the strongest artificial intelligence techniques for complex system modeling. In this system, an FCM based on Nonlinear Hebbian Learning (NHL) algorithm is used. The data used in this study are collected from the medical records of 560 patients referring to Imam Reza Hospital in Tabriz City. 27 effective features in gastric cancer were selected using the opinions of three experts. The prediction accuracy of the proposed method is 95.83%. The results show that the proposed method is more accurate than other decision-making algorithms, such as decision trees, Naïve Bayes, and ANN. From the perspective of healthcare professionals, the proposed medical decision support system is simple, comprehensive, and more effective than previous models for assessing the risk of GC and can help them to predict the risk factors for GC in the clinical setting.
Highlights
Gastric cancer (GC) which is one of the major cancers around the world with about one million new patients each year is known to be the third cause of cancer deaths [1, 2]
The proposed model presented in this study is attempting to rationalize beyond the analyses of clinical experts and increase the ability of experts to make logical decisions in a clinical setting for patients with different levels of risk factors for GC and help clinical specialists to make a logical decision about optimal preventive methods for patients
The 95.8% overall classification accuracy obtained through the Hebbian-based fuzzy cognitive maps (FCMs) using 560 patients indicates a high level of coordination between the proposed system and medical decisions, and the proposed decision support tool can be trusted for clinical professionals and helps them in the process of risk assessment of gastric GC
Summary
Gastric cancer (GC) which is one of the major cancers around the world with about one million new patients each year is known to be the third cause of cancer deaths [1, 2] This represents an important public health issue in the world, especially in Central Asian countries, where the incidence of this disease is very high [2]. GC is a multifactorial disease, and its formation is related to various risk factors [3] Various scientific methods, such as photofluorography and esophagogastroduodenoscopy, are used to diagnose GC in the early stages and can help reduce the mortality rate of GC with a practical approach [3]. Appropriate prevention efforts can be made to reduce the incidence of this disease
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Computational and Mathematical Methods in Medicine
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
