International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Maternal Risk Prediction using Machine Learning |
|---|---|
| ABSTRACT | Maternal morbidity and mortality remain serious global health concerns despite advancements in medical care. Many pregnancy-related complications can be prevented through early identification of high-risk cases. Traditional risk assessment methods rely heavily on manual evaluation and often fail to capture complex relationships among multiple health factors. Machine learning techniques provide an effective solution by analyzing large healthcare datasets and identifying patterns that are difficult to detect using conventional approaches. This paper presents a simplified review of maternal risk prediction using machine learning. It discusses major maternal risk factors, commonly used machine learning algorithms, data sources, and key challenges related to bias, privacy, and model interpretability. The study highlights the potential of machine learning systems to support clinicians in early risk detection and improve maternal health outcomes. |
| AUTHOR | SINDHU K M, HARSHA G V, SACHIN R A, GURURAJ K R, VARUN G T Assistant Professor, Dept. of ISE, Jain Institute of Technology, Davangere, Karnataka, India UG Student, Dept. of ISE, Jain Institute of Technology, Davangere, Karnataka, India |
| VOLUME | 180 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1401071 |
| pdf/71_Maternal Risk Prediction using Machine Learning.pdf | |
| KEYWORDS | |
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