Machine Learning Algorithm for Breastmilk Quality Classification Using Multi-Array Sensor Technology: A Systematic Literature Review

M. Shahkhir Mozamir, Iqlima Nadhira Jamaludin, Shafina Binti Abd Karim Ishigaki, Fatin Aliah Binti Yahya and Anggi Muhammad Rifa'i
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M. Shahkhir Mozamir: Faculty Technology Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
Iqlima Nadhira Jamaludin: Faculty Technology Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
Shafina Binti Abd Karim Ishigaki: Faculty Technology Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
Fatin Aliah Binti Yahya: Faculty Technology Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia
Anggi Muhammad Rifa'i: Faculty Technology Maklumat dan Komunikasi, Universiti Teknikal Malaysia Melaka (UTeM), Malaysia

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 9, 9680-9698

Abstract: Assessing breastmilk quality is essential to ensure optimal nutrition for infants. However, conventional laboratory-based methods are often time-consuming, costly, and impractical for real-time applications. Recent advancements in multi-array sensor technology combined with machine learning algorithms present a promising solution for efficient and accurate breastmilk classification. This systematic literature review aims to evaluate existing research on the integration of sensor technologies and machine learning models for breastmilk quality assessment. It specifically addresses the shortcomings of traditional approaches and explores the feasibility of real-time monitoring systems. Following the PRISMA guidelines, research articles were initially collected at 42 poaper published between 2020 and 2025. The papers identified from IEEE Xplore, Scopus, ScienceDirect, and Google Scholar databases. After applying inclusion criteria and a through screening process, unfortunately only 5 research papers were selected based on their relevance to sensor integration, machine learning algorithms, dataset characteristics, and classification performance. Applying machine learning for breastmilk classification is arising by year. The findings categorize existing approaches into traditional statistical methods, machine learning techniques, and deep learning models. Random Forest and Support Vector Machines (SVM) emerged as commonly used classifiers due to their balance between accuracy and computational efficiency. Although deep learning models show potential for improved accuracy, they require larger datasets and greater processing power. Feature extraction and selection significantly influence classification outcomes by identifying key breastmilk components. This review provides a foundation for developing real-time breastmilk quality monitoring systems using multi-array sensors and machine learning, offering valuable insights for advancing maternal and infant healthcare technologies.

Date: 2025
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