Intelligent Web-Based Application for Personalized Obesity Management

Obesity is a serious global problem due to its association with various chronic diseases. This study explores the utilization of machine learning in particular deep learning technology to predict Body Mass Index (BMI) from individual photos to create an efficient solution for assessing obesity. Using the ResNet152 model and K-Fold Cross Validation, this application integrates filters on individual photos to improve prediction accuracy. The application was developed using React JS for the front end, PHP and MySQL for the backend and database management, and Python as the core of the machine learning system. The application that tested using blackbox method, to see all features is functioning and the web application prototipe is passed all the test scenario.

Authors

Publication Details

Journal
Journal of Applied Informatics and Computing
Published
2025-06-05
DOI
https://doi.org/10.30871/jaic.v9i3.9151
Citations
1
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
1.28
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article

Intelligent Web-Based Application for Personalized Obesity Management

Made Sudarma, I Ketut Gede Darma Putra, Oka Sudana, I Gusti Ngurah Lanang Wijayakusuma et al.
1 citations
Journal of Applied Informatics and Computing
Context-Aware Activity Recognition Systems
1.28
article

Intelligent Web-Based Application for Personalized Obesity Management

Made Sudarma, I Ketut Gede Darma Putra, Oka Sudana, I Gusti Ngurah Lanang Wijayakusuma, Min-Ho Jo
article en
1 citations

Abstract

Obesity is a serious global problem due to its association with various chronic diseases. This study explores the utilization of machine learning in particular deep learning technology to predict Body Mass Index (BMI) from individual photos to create an efficient solution for assessing obesity. Using the ResNet152 model and K-Fold Cross Validation, this application integrates filters on individual photos to improve prediction accuracy. The application was developed using React JS for the front end, PHP and MySQL for the backend and database management, and Python as the core of the machine learning system. The application that tested using blackbox method, to see all features is functioning and the web application prototipe is passed all the test scenario.

Journal of Applied Informatics and ComputingVol. 9(3)
Openalex Percentile: Top 18%
Context-Aware Activity Recognition Systems
1.28
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