Artificial intelligence in malnutrition research: a bibliometric  analysis

Herman  Yuliansyah; Sulistyawati  Sulistyawati; Tri Wahyuni  Sukesi; Surahma Asti  Mulasari; Wan Nur Syamilah  Wan Ali

doi:10.31763/businta.v7i1.605

Authors

Herman Yuliansyah Faculty of Industrial Technology, Universitas Ahmad Dahlan, Indonesia
Sulistyawati Sulistyawati Faculty of Public Health, Universitas Ahmad Dahlan, Indonesia
Tri Wahyuni Sukesi Faculty of Public Health, Universitas Ahmad Dahlan, Indonesia
Surahma Asti Mulasari Faculty of Public Health, Universitas Ahmad Dahlan, Indonesia
Wan Nur Syamilah Wan Ali Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia

DOI:

https://doi.org/10.31763/businta.v7i1.605

Keywords:

Malnutrition, Artificial intelligence, Machine learning, Neural networks, Deep learning, Bibliometric analysis

Abstract

Malnutrition is a nutritional imbalance in a child’s body. Currently, there have been many reviews done on malnutrition in children. However, reviews on artificial intelligence linked with malnutrition are yet to be done. Thus, this study aims to identify the implementation of artificial intelligence in predicting malnutrition using bibliometric analysis. The bibliometric analysis consists of four stages: determining the purpose and scope, selecting the analytical technique, collecting data, and presenting the findings. Data used for this analysis is sourced from the Scopus database. The investigation was conducted using VOSviewer and “Publish or Perish” software. Based on five searched words: malnutrition, artificial intelligence, machine learning, neural networks, and deep learning, it was found that machine learning is the most widely used artificial intelligence approach for malnutrition research. Deep learning techniques are reported to grow as it is introduced as a new method in artificial intelligence. Malnutrition prediction tasks are the most studied problem. The use of deep learning, reinforcement learning, and transfer learning methods are used tremendously in malnutrition prediction research. This analysis’s results help improve the quality of the review by showing the mapping areas for malnutrition research.

Author Biographies

Herman Yuliansyah, Faculty of Industrial Technology, Universitas Ahmad Dahlan, Indonesia

Wan Nur Syamilah Wan Ali, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia

Ph.D. student at the Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor 43600, Malaysia

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