Harnessing Food Waste for IBS Management using AI

Riya Mary Raju; Pranav J; Karthik Santhosh; Vaishnavi S; Soma Maji

doi:10.63363/aijfr.2026.v07i03.5352

Harnessing Food Waste for IBS Management using AI

Author(s)	Ms. Riya Mary Raju, Mr. Pranav J, Mr. Karthik Santhosh, Ms. Vaishnavi S, Prof. Dr. Soma Maji
Country	India
Abstract	Irritable Bowel Syndrome (IBS) is a chronic functional gastrointestinal disorder affecting approximately 7–21% of the global population, significantly impairing quality of life and productivity. Despite its prevalence, personalized dietary intervention remains underexplored, with most clinical guidance relying on generalized recommendations that fail to account for individual subtype variation, lifestyle influences, and specific nutritional tolerances. Simultaneously, agro-industrial food processing generates substantial quantities of nutrient-dense byproducts — including rice bran, banana peels, apple cores, carrot tops, soy hulls, broccoli stalks, coconut husks, and potato peels — that are routinely discarded, representing a dual economic and environmental loss. This study presents the IBS Food Waste Dietary Advisor, a full-stack machine learning application designed to bridge these two challenges simultaneously. A synthetic dataset of 500 IBS patient records comprising 38 clinically informed variables — including IBS subtype, nutritional intake, FODMAP classification, lifestyle factors, and multi-domain symptom scores — was generated using domain-informed correlation rules to train a Random Forest Classifier with 200 decision trees. The model achieved an overall accuracy of 85%, with class-level precision ranging from 83–87%, recall from 83–88%, and F1-scores between 0.84 and 0.88. The system was deployed via a Flask REST API backend and a React.js multi-step frontend questionnaire, delivering confidence-scored, FODMAP-aware personalized food recommendations in under 100 milliseconds per request. Key dataset insights revealed that high FODMAP exposure combined with elevated stress levels significantly correlated with increased pain and bloating scores, while adequate sleep (7+ hours nightly) was associated with meaningfully reduced urgency. Soluble fiber from byproducts such as rice bran and banana peel improved Bristol Stool Scale consistency in IBS-C patients. This work contributes a novel, sustainable framework at the intersection of food technology and artificial intelligence, demonstrating that agro-industrial waste can be therapeutically repurposed to support gut health, reduce environmental burden, and advance personalized nutrition in chronic disease management.
Keywords	Irritable Bowel Syndrome, FODMAP, food waste upcycling, Random Forest, personalized nutrition, dietary recommendation system, machine learning, gut health, circular economy
Published In	Volume 7, Issue 3, May-June 2026
Published On	2026-05-01
DOI	https://doi.org/10.63363/aijfr.2026.v07i03.5352

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Harnessing Food Waste for IBS Management using AI

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