A BIG DATA DRIVEN NUTRITIONAL ANALYSIS USING CONSTANT TIME KNN ENSEMBLE LEARNING
R Abinaya
, Dr M Caroline Viola Stella Mary
Nutritional Analysis, Big Data, Machine Learning, Ensemble Learning, Constant-Time K-Nearest Neighbors (KNN), KMeans Clustering, Model Evaluation Metrics, Confusion Matrix Analysis
In recent years, understanding food nutrient composition has become crucial for promoting healthier dietary choices and preventing nutrition-related health issues. This study presents a big data-driven approach to nutritional analysis, utilizing Constant-Time k-Nearest Neighbors (KNN) Ensemble Learning to efficiently classify and analyze nutrient profiles of various food products. By leveraging a large-scale dataset, the proposed methodology aims to provide a high-speed, accurate classification of key nutrients such as cholesterol, protein, lipids, and sodium. In addition, a Random Forest model serves as a comparative baseline, highlighting the performance strengths and weaknesses of different machine learning techniques. Extensive model evaluations include accuracy assessments, feature importance analysis, and confusion matrix visualizations. The results underscore the benefits of KNN Ensemble Learning in handling extensive datasets and demonstrate its potential to aid in public health initiatives by enhancing the precision of food nutrient information. This work contributes to the development of scalable, data-driven tools that can support informed dietary decisions and public health policies.
"A BIG DATA DRIVEN NUTRITIONAL ANALYSIS USING CONSTANT TIME KNN ENSEMBLE LEARNING", IJNRD - INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT (www.IJNRD.org), ISSN:2456-4184, Vol.9, Issue 10, page no.d33-d45, October-2024, Available :https://ijnrd.org/papers/IJNRD2410306.pdf
Volume 9
Issue 10,
October-2024
Pages : d33-d45
Paper Reg. ID: IJNRD_301644
Published Paper Id: IJNRD2410306
Downloads: 00021
Research Area: Science and Technology
Country: Vannarpettai / Thirunelveli, Tamilnadu , India
ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJNRD (IJ Publication) Janvi Wave
