International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Volume 8 Issue 2
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PregBot: an ML and NLP-Based Intelligent System Supporting Women and Families during Pregnancy

Author(s) Mr. Malavath Sai Deepak, Mr. Gunti Ashok, Mr. Banoth Sachin
Country India
Abstract Pregnancy involves complex physical and emotional changes that demand accurate information and timely support. Many expectant mothers experience difficulty in obtaining immediate guidance, which may result in confusion, stress, and delayed healthcare decisions. This study introduces PregBot, an AI-driven intelligent assistant designed using Machine Learning (ML) and Natural Language Processing (NLP) to provide continuous support for pregnant women and their families. The system is capable of understanding natural language queries, identifying user intent, evaluating reported symptoms, and estimating potential risk levels. NLP techniques enable conversational interaction and contextual interpretation, while ML models perform symptom severity classification and predictive analysis. In addition, a sentiment analysis component detects emotional states to generate more adaptive and supportive responses. The proposed framework focuses on delivering accessible, real-time, and personalized assistance to enhance maternal awareness and well-being. Performance evaluation indicates accurate intent detection, dependable risk assessment, and effective user interaction, demonstrating the potential of intelligent conversational systems in pregnancy-related healthcare applications.
Keywords Machine Learning (ML), Natural Language Processing (NLP), Healthcare Chatbot, Maternal Health Assistance, Symptom Analysis, Risk Prediction, Emotion Detection
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 8, Issue 2, March-April 2026
Published On 2026-03-05
DOI https://doi.org/10.36948/ijfmr.2026.v08i02.70584
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E-ISSN 2582-2160
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