ICTACT Journal on Image and Video Processing

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Predictive Modeling of Gastric Disease Progression from Endoscopic Images using Fuzzy Logic and Machine Learning


Affiliations
1 School of Business, GITAM University, India
2 Department of Science and Humanities, RMK College of Engineering and Technology, India
3 Department of Computer Science and Engineering, N.S.N. College of Engineering and Technology, India
4 Department of Computer Applications, Mepco Schlenk Engineering College, India
5 College of Computing and Information Sciences, University of Technology and Applied Sciences, Oman

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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.

Keywords

Gastric Disease, Endoscopic Images, Fuzzy Logic, XGBoost, Predictive Modeling
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  • Predictive Modeling of Gastric Disease Progression from Endoscopic Images using Fuzzy Logic and Machine Learning

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Authors

Somasekhar Donthu
School of Business, GITAM University, India
S. Poongothai
Department of Science and Humanities, RMK College of Engineering and Technology, India
A. Rajesh Kumar
Department of Computer Science and Engineering, N.S.N. College of Engineering and Technology, India
A.D.C. Navin Dhinnesh
Department of Computer Applications, Mepco Schlenk Engineering College, India
D.R. Prince Williams
College of Computing and Information Sciences, University of Technology and Applied Sciences, Oman

Abstract


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.

Keywords


Gastric Disease, Endoscopic Images, Fuzzy Logic, XGBoost, Predictive Modeling