Machine Learning Driven Pregnancy Care System

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Kunal Rajesh Parge; Pratiksha Sandip Jagtap; Tanishka Atul Pawar; Preeti Dnyanoba Kadam

Machine Learning Driven Pregnancy Care System

Authors: Kunal Rajesh Parge, Pratiksha Sandip Jagtap, Tanishka Atul Pawar, Preeti Dnyanoba Kadam

Unique Paper ID: 179952
Volume: 11
Issue: 12
PageNo: 8577-8584

Keywords: maternal health data analytics AI Maternal Risk Factors

Abstract:
Machine learning (ML) techniques have revolutionized the healthcare domain by enabling the development of intelligent systems that can learn from health data and make accurate predictions. This paper presents an ML-driven pregnancy care system that analyzes key physiological parameters such as age, blood pressure, blood sugar, body temperature, heart rate and many more to predict potential pregnancy risks and detect diseases such as Anemia, Thrombocytopenia, Thalassemia, and Gestational Diabetes. The implemented system utilizes supervised learning algorithms like Random Forest and SVM for accurate classification of risk levels and disease status. In addition to prediction, the system offers trimester wise personalized diet plans and disease-specific dietary and treatment recommendations. By integrating ML into maternal healthcare, this approach enhances early detection of complications, supports clinical decision- making, and promotes better outcomes for both mother and child—especially in areas with limited access to healthcare professionals. The system aims to deliver accessible, data-driven, and personalized care throughout pregnancy.

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Cite This Article

ISSN: 2349-6002
Volume: 11
Issue: 12
PageNo: 8577-8584

Machine Learning Driven Pregnancy Care System

Available:https://ijirt.org/article?manuscript=179952

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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