A person's Body Mass Index, or BMI, is the most widely used parameter for assessing their health. BMI is a crucial predictor of potential diseases that may arise at higher body fat levels because it is correlated with body fat. Conversely, a community's or an individual's nutritional status can be determined using the BMI. Although deep learning models are used in several studies to estimate BMI from face photos and other data, no previous research established a clear connection between deep learning techniques for handwriting analysis and BMI prediction. This article addresses this research gap with a deep learning approach to estimating BMI from handwritten characters by developing a convolutional neural network (CNN). A dataset containing samples from 48 people in lowercase English scripts is successfully captured for the BMI prediction task. The proposed CNN-based approach reports a commendable accuracy of 99.92%. Performance comparison with other popular CNN architectures reveals that AlexNet and InceptionV3 achieve the second and third-best performance, with the accuracy of 99.69% and 99.53%, respectively.
@article{arxiv.2409.02584,
title = {BMI Prediction from Handwritten English Characters Using a Convolutional Neural Network},
author = {N. T. Diba and N. Akter and S. A. H. Chowdhury and J. E. Giti},
journal= {arXiv preprint arXiv:2409.02584},
year = {2025}
}
Comments
The manuscript is being withdrawn due to identified issues that require substantial revision and additional experiments. We plan to address these concerns and resubmit a revised version in the future