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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | CHILD AND MATERNAL MORTALITY RISK FACTOR ANALYSIS USING MACHINE LEARNING |
| Authors Name: | Nandhish L , Kushaal R , Pavan Kumar s , A.Lavanya |
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IJRTI_211263
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| Published Paper Id: | IJRTI2604114 |
| Published In: | Volume 11 Issue 4, April-2026 |
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| Abstract: | Maternal and child mortality remain significant public health challenges, particularly in low- and middle-income countries where early risk identification and continuous healthcare monitoring are often limited. Previous studies have shown that machine learning techniques can effectively predict mortality risk using clinical indicators, with models such as Random Forest achieving very high accuracy after addressing class imbalance through over-sampling and under-sampling methods. However, most existing approaches function mainly as analytical models and lack integration into real-time, end-to-end healthcare decision-support systems. The proposed system aims to overcome these limitations by developing a comprehensive early-warning and decision support platform for maternal and child health management. The system analyzes a wide range of health-related risk factors, including demographic details, nutritional status, medical history, pregnancy-related complications, vaccination records, and socio-economic conditions, to classify cases into low-, moderate-, or high-risk categories. Beyond prenatal prediction, the system also evaluates post-delivery health conditions of both the mother and newborn by monitoring postpartum complications, neonatal vital signs, infection risks, and early growth indicators.Machine learning models such as Logistic Regression, Support Vector Machine (SVM), Neural Networks, and Random Forest are trained using historical healthcare datasets to identify critical patterns, generate accurate risk predictions, and determine key contributing factors. By providing timely risk assessment and actionable insights, the proposed system supports healthcare professionals in early intervention and improved decision-making, ultimately contributing to the reduction of maternal and child mortality rates. |
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| Cite Article: | " CHILD AND MATERNAL MORTALITY RISK FACTOR ANALYSIS USING MACHINE LEARNING ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a809-a811, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604114.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604114
Registration ID:211263
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a809-a811 Country: Hosur, Tamil Nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604114 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604114 |
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ISSN: 2456-3315
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