Gastric disease progression is challenging to predict due to the complex nature of endoscopic images. This study addresses the problem by integrating fuzzy logic with machine learning, specifically XGBoost, for predictive modeling. The proposed method preprocesses endoscopic images, extracts features, and applies fuzzy logic for classification, followed by XGBoost for final prediction. Results demonstrate an accuracy of 92.5% and an F1-score of 0.91, outperforming traditional methods. The model offers a robust tool for early detection and monitoring of gastric diseases, enhancing clinical decision-making.
Somasekhar Donthu1, S. Poongothai2, A. Rajesh Kumar3, A.D.C. Navin Dhinnesh4, D.R. Prince Williams5
GITAM University, India1, RMK College of Engineering and Technology, India2, N.S.N. College of Engineering and Technology, India3, Mepco Schlenk Engineering College, India4, University of Technology and Applied Sciences, Sultanate of Oman5
Gastric Disease, Endoscopic Images, Fuzzy Logic, XGBoost, Predictive Modeling
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 1 , Pages: 3379 - 3383 )
Date of Publication :
August 2024
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51
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