Abstract :

Maternal Health Remains A Critical Issue Worldwide, Especially In Developing Regions Where Access To Prompt And Effective Medical Care Is Often Limited. Detecting Potential Pregnancy Complications At An Early Stage Is Essential For Protecting The Lives Of Mothers And Newborns. With The Rapid Growth Of Artificial Intelligence (AI), Machine Learning (ML), And Data Science Technologies, Healthcare Organizations Can Now Utilize Intelligent Predictive Systems To Support Clinical Decision-making And Improve Patient Outcomes. This Project Proposes An Intelligent Maternal Health Risk Prediction System That Applies Multiple Machine Learning Methodologies Categorized As White Box, Grey Box, And Black Box Models. The Framework Utilizes Maternal Healthcare Data Containing Various Medical And Demographic Factors, Including Maternal Age, Blood Pressure Measurements, Glucose Concentration, Body Temperature, Heart Rate, And Pregnancy-related Information. To Ensure Data Quality And Model Effectiveness, Preprocessing Techniques Such As Data Cleaning, Normalization, And Feature Selection Are Performed Before Model Development. Several Classification Algorithms Are Employed And Compared, Including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine (SVM), And Convolutional Neural Network (CNN). These Models Are Trained To Identify Maternal Health Conditions And Categorize Patients Into Three Risk Levels: Low Risk, Medium Risk, And High Risk. The Generated Predictions Can Assist Healthcare Practitioners In Recognizing Vulnerable Patients And Initiating Appropriate Medical Interventions At An Earlier Stage. Performance Evaluation Demonstrates That Machine Learning And Deep Learning Approaches Can Effectively Predict Maternal Health Risks With High Accuracy. The Proposed Solution Enhances Clinical Decision Support, Improves Patient Monitoring, And Contributes To Better Allocation Of Healthcare Resources. By Facilitating Early Identification Of High-risk Pregnancies, The System Has The Potential To Reduce Preventable Maternal Complications And Mortality. Additionally, The Framework Is Designed To Support Future Expansion Through Integration With Cloud Computing Platforms, Internet Of Things (IoT) Devices, And Real-time Healthcare Monitoring Systems, Making It Suitable For Next-generation Smart Healthcare Applications.

Published:

09-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

331-343

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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