The application of machine learning for infant cries classification and pathological cries detection: A systematic review
Issued Date
2026-01-01
Resource Type
ISSN
00368504
eISSN
20477163
Scopus ID
2-s2.0-105027562963
Pubmed ID
41533681
Journal Title
Science Progress
Volume
109
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Science Progress Vol.109 No.1 (2026)
Suggested Citation
Sirithepmontree S., Katchamat N., Nuampa S. The application of machine learning for infant cries classification and pathological cries detection: A systematic review. Science Progress Vol.109 No.1 (2026). doi:10.1177/00368504251410776 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/114712
Title
The application of machine learning for infant cries classification and pathological cries detection: A systematic review
Author(s)
Corresponding Author(s)
Other Contributor(s)
Abstract
Objective: This study aims to systematically review and synthesize the studies on the application of machine learning for classifying infant cry types, identifying pathological cries, and evaluating the accuracy of infant cry recognition. Methods: This review followed the PRISMA guidelines and was registered in PROSPERO (CRD42024600969). The literature search was conducted on four data sources: PubMed, CINAHL, Embase, and IEEE Xplore. The included studies focused on machine learning-based classification of infants’ needs cries or pathological cries. These were published in English between January 1, 2014 and October 31, 2024. Study quality was assessed using the QUADAS-2 tool. Results: Of 919 studies were identified, 17 were included in the final synthesis. Machine learning can classify infant cries into two main types: infant needs’ cries and pathological cries, with some studies addressing both. Needs-related cries comprised nine subtypes, while pathological cries included six subtypes. Classification accuracy varied by machine learning classifier and the features used, ranging from 44.5% to 99.82%. The highest accuracy for infant needs’ cries was hunger and pain cries at 99.82% using a Gaussian mixture model (GMM) classifier with constant-Q cepstral coefficients features. For pathological cries, the highest accuracy was for detecting deafness (99.42% to 99.82%), using a genetic selection of Fuzzy Model and a GMM classifier. Conclusions: Machine learning shows strong potential for accurately classifying infant cries and detecting pathologies. Future research should prioritize developing diverse cry datasets to improve model generalizability, evaluating performance in real-world settings, and integrating cry analysis with physiological signals to enhance diagnostic accuracy.