Original scientific paper
https://doi.org/10.1080/00051144.2024.2318168
A sustainable health and educational goal development (SHEGD) prediction using metaheuristic extreme learning algorithms
R. Jagadeesh Kannan
; Department of Computer Science and Engineering, SRM Institute of Science and Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India
*
Muraleedharan Manningal
; Department of Computer Science and Engineering, SRM Institute of Science and Technology Tiruchirappalli, Tiruchirappalli, Tamil Nadu, India
* Corresponding author.
Abstract
The United Nations established the 17 Sustainable Development Goals (SDGs) in 2015 to address
issues like gender equality, clean water, health, education, and hunger by 2030. Of the 17 SDGs,
health and education have an outsized impact on countries’ socioeconomic development, so
providing insights into progress on these two goals is crucial. Machine learning can help solve
many real-world problems, including working towards the SDGs. This paper proposes using
a metaheuristic ensemble of Cat Swarm Optimization algorithms with Feed Forward Extreme
Learning Machines, called Sustainable Health And Educational Goal Development (SHEGD)
Prediction, to effectively contribute to countries’ economic growth by achieving health and
education SDGs through machine learning. The model is assessed using UN SDG datasets and
performance metrics like accuracy, precision, recall, specificity, and F1-score. Comparisons to
other machine learning models demonstrate this model’s superiority in designing a recommendation system for progressing towards the health and education SDGs. The proposed model
outperforms the other approaches, proving its value for an SDG recommendation system design.
Keywords
Sustainable development goals; artificial intelligence; machine learning; cat swarm optimization; feed forward extreme learning machines
Hrčak ID:
326081
URI
Publication date:
19.2.2024.
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