RESEARCH ARTICLE
Decision-making Support System for Predicting and Eliminating Malnutrition and Anemia
Manasvi Jagadeesh Maasthi1, Harinahalli Lokesh Gururaj2, *, Vinayakumar Ravi3, *, Basavesha D4, Meshari Almeshari5, Yasser Alzamil5
Article Information
Identifiers and Pagination:
Year: 2023Volume: 16
E-location ID: e18750362246898
Publisher ID: e18750362246898
DOI: 10.2174/0118750362246898230921054021
Article History:
Received Date: 10/02/2023Revision Received Date: 10/07/2023
Acceptance Date: 19/07/2023
Electronic publication date: 27/10/2023
Collection year: 2023
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Aims:
This study investigates predicting and eliminating malnutrition and anemia using ML Algorithms and comparing all the methods with various malnutrition-based parameters.
Background:
The health of the nation is more important than the wealth of the nation. Malnutrition and anemia are serious health issues but the least importance is given to eradicate them.
Objective:
Proper nutrition is an essential component for the survival, growth, and development of infants, children, and women who in turn give birth to infants.
Methods:
In the proposed system, machine learning approaches are utilized to predict the malnutrition status of children under five years of age and anemia in men and women using old datasets and further providing a suitable diet recommendation to overcome the disease. Classification techniques are being used for malnutrition as well as anemia prediction.
Results:
Algorithms such as Naïve Bayes classifier (NBC), Decision Tree (DT) algorithm, Random Forest (RF) algorithm, and K-Nearest Neighbor (k-NN) algorithm are utilized for prediction. The results obtained by these algorithms are 94.47%, 85%, 95.49%, and 63.15%. When compared, Naïve Bayes and random forest algorithm provided efficient results for malnutrition and anemia, respectively.
Conclusion:
By testing and validating, preventive actions can be taken with the help of medical experts and dieticians to reduce malnutrition and anemia conditions among children and elders, respectively.
