Systematic Literature Review: Pain Detection in Infants Using Machine Learning

Fajar Adhitya  Kamal; Richie  Fairlee; Rodrick  Tristan; Christnatalis  HS

doi:10.54783/ijsoc.v8i1.1648

Fajar Adhitya Kamal University Prima Indonesia, Medan, Indonesia
Richie Fairlee University Prima Indonesia, Medan, Indonesia
Rodrick Tristan University Prima Indonesia, Medan, Indonesia
Christnatalis HS University Prima Indonesia, Medan, Indonesia

DOI: https://doi.org/10.54783/ijsoc.v8i1.1648

Keywords: Pain Detection, Infants, Machine Learning, Facial Expressions, Systematic Literature Review.

Abstract

Detecting pain in infants is a significant challenge in the medical field because infants are unable to verbally express pain, meaning healthcare professionals must rely on subjective observations of behaviour and physiological responses. With the advancement of artificial intelligence technology, various studies have developed automated systems based on machine learning to detect pain through the analysis of facial expressions, crying sounds, and physiological signals such as heart rate and Electrodermal Activity (EDA). This study aims to identify the developments, methods, and technological trends in pain detection in infants through a Systematic Literature Review (SLR) approach. The research process followed the PRISMA 2020 guidelines, with data sources from Google Scholar, Scopus, Crossref, and OpenAlex covering the period 2015–2025. Based on the inclusion and exclusion criteria, 31 relevant studies were identified for analysis. The analysis covered data types, Machine Learning methods, and quality assessment using the GRADE approach. The results of the review indicate that the most commonly used algorithms are Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Random Forests, and Recurrent Neural Networks (RNN). Overall, the application of Machine Learning shows great potential in improving the objectivity and accuracy of pain detection in infants, although further research is still required to develop a more optimal and clinically applicable system.

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