A Predictive Nutritional Assessment System for Vegetarians using Artificial Neural Networks

This study presents the development of a predictive nutritional assessment system using an Artificial Neural Network (ANN) tailored for adult vegetarians. The system integrates biometric, dietary, and lifestyle data derived from the Anthropometric, Biochemical, Clinical, and Dietary (ABCD) nutritional assessment framework to classify individuals into undernourished, normal, and overweight categories. A feedforward ANN architecture was implemented with 20 input nodes representing ABCD indicators, one hidden layer of 10 neurons, and an output layer of 3 nodes. The model was trained using backpropagation over 1000 epochs with a learning rate of 0.5 and momentum of 0.9, employing data normalization and one-hot encoding for categorical variables. The dataset was split into 80% for training and 20% for testing. The proposed system achieved a classification accuracy of 92.4%, with a 65.3% reduction in cross-entropy loss and an average precision of 89.7% across classes, while processing each input in under 0.8 seconds. Nutritional status predictions were validated using BMI thresholds and cross-referenced with clinical and dietary indicators. Compared to traditional assessment methods, the ANN significantly reduces processing time, improves scalability, and enables dynamic feedback through evolving health indicators. Features such as structured meal plans, real-time daily nutrition summaries, and tailored exercise recommendations enhanced user engagement and adherence to dietary guidelines. The results demonstrate the ANN’s potential as a reliable and scalable tool for personalized nutritional evaluation, enabling early intervention and adaptive planning to address the specific dietary needs and nutrient deficiencies commonly found in vegetarian populations, ultimately supporting predictive healthcare and dietary optimization.