AI-Powered Personalized Nutrition: A Deep Learning Framework for Predicting Dietary Interventions Based on Gut Microbiome Profiles in Indian Populations

Abstract

Personalized nutrition is emerging as a transformative approach in preventive healthcare, particularly with advances in gut microbiome research. However, most existing studies and dietary algorithms are based on Western populations, overlooking the genetic, dietary, and cultural diversity of the Indian population. This study proposes a novel deep learning-based framework that utilizes gut microbiome profiles to predict personalized dietary recommendations tailored to Indian individuals. By collecting stool samples and lifestyle data from 300 diverse participants across India, the microbial composition was analyzed using 16S rRNA sequencing. A hybrid deep neural network model combining convolutional and recurrent layers was developed to identify microbiome patterns associated with metabolic disorders such as diabetes, obesity, and inflammatory bowel diseases. The model predicts optimal dietary interventions, integrating both traditional Indian foods and modern nutritional guidelines. Validation showed a prediction accuracy of 91.3%, with significant improvements in participant health indicators after a 12-week trial. This framework has the potential to revolutionize personalized diet planning by considering India's unique microbiome landscape. It also offers a scalable AI model for broader public health applications.