Evaluation of Support Vector Machine and Kernel Neural Network Classification for Fast Food Nutrition Data

With the development of technology, maintaining a healthy lifestyle has gotten simpler by keeping track of everyday activities like how much food you eat and what you eat. Nutritional study of fast food aims to prevent chronic diseases. Healthy nutrition and unhealthy nutrition are the two classific...

Full description

Saved in:
Bibliographic Details
Published in2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) pp. 150 - 154
Main Authors Senthilmurugan, M., Yamsani, Nagendar, M J, Carmel Mary Belinda, S, Loganayagi, Padmakala, S., Akilandeswari, A.
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.08.2023
Subjects
Analytical models
Feature extraction
Food Nutrition
Independent component analysis
KNN
Machine learning
Neural networks
PCA
Simulation
Support vector machines
SVM
Online AccessGet full text

Cover

More Information
Summary:With the development of technology, maintaining a healthy lifestyle has gotten simpler by keeping track of everyday activities like how much food you eat and what you eat. Nutritional study of fast food aims to prevent chronic diseases. Healthy nutrition and unhealthy nutrition are the two classifications. The support vector machine (SVM) with PCA, T-SNE is compared to KNN with PCA, and T-SNE to categorize this nutrition value. Kaggle food nutrition database is used and evaluated in order to test food. SVM with T-SNE outperforms KNN in terms of performance.
DOI:10.1109/ICAISS58487.2023.10250603