Leveraging IoT and Machine Learning for Enhancing Women’s Safety: A Wearable-based Solution

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 02 | Feb 2025

p-ISSN: 2395-0072

www.irjet.net

Leveraging IoT and Machine Learning for Enhancing Women’s Safety: A Wearable-based Solution Polavarapu Nagendra Babu1, Dr. B. MaheshBabu2 1

PG-Scholar,M.Tech-CSE(ArtificialIntelligenceandMachineLearning),SRGEC,Gudlavalleru,India Associate.Professor,DepartmentofElectricalandElectronicsEngineering,SRGEC,Gudlavalleru,India ---------------------------------------------------------------------***--------------------------------------------------------------------2

Abstract-PregnancyBot is an intelligent virtual assistant designed to enhance maternal healthcare by integrating deep learning (DL) and transformer-based natural language processing (NLP) models. The system offers personalized support to expectant mothers by analyzing user interactions and health-related queries using Bidirectional Encoder Representations from Transformers (BERT). PregnancyBot provides real-time recommendations on nutrition, physical activity, and mental well-being while addressing concerns through empathetic and context-aware conversations. A reinforcement learning-based adaptive feedback mechanism enables the system to refine responses based on user engagement, ensuring continuous improvement. Additionally, PregnancyBot incorporates a secure federated learning approach to maintain user privacy while leveraging collective insights for improved maternal health guidance. The platform also integrates seamlessly with electronic health records (EHR), enabling collaboration with medical professionals. By offering an interactive and data-driven support system, PregnancyBot aims to empower pregnant women, enhance healthcare accessibility, and foster a positive maternal experience.

machine learning- driven predictive analytics with conversational AI, PregnancyBot aims to bridge the gap between traditional maternal healthcare and AIpowered digital assistance.

One of the primary concerns during pregnancy is the accessibility of reliable health information. Many expectant mothers rely on online sources, which can often be misleading, contradictory, or lacking in credibility. PregnancyBot addresses this issue by employing transformer-based NLP models, such as Bidirectional Encoder Representations from Transformers (BERT) and Generative Pre-trained Transformer (GPT), to process vast amounts of medical literature, clinical guidelines, and user queries. This enables PregnancyBot to engage in context-aware and empathetic conversations, offering precise and scientifically validated responses. Unlike static healthcare chatbots, PregnancyBot continuously refines its knowledge base through reinforcement learning, allowing it to adapt to individual user needs, preferences, and concerns over time. By analyzing historical user interactions, PregnancyBot ensures that the support it provides is dynamic, evolving, and relevant to each stage of pregnancy, from conception to postpartum care.

Keywords: Deep Learning, Transformer Models, Maternal Healthcare, Virtual Assistant, Federated Learning, Pregnancy Support

Another crucial aspect of pregnancy care is the need for personalized health monitoring and recommendations.

PregnancyBot integrates federated learning, a decentralized AI approach that allows it to learn from a wide range of user interactions while preserving data privacy. This enables PregnancyBot to offer customized nutritional plans, exercise routines, mental health support, and medical insights without compromising sensitive user information. Additionally, it connects with electronic health records (EHRs) and remote healthcare platforms, facilitating seamless communication between users and medical professionals. By analyzing user data trends and medical histories, PregnancyBot can proactively detect potential risks such as gestational diabetes, hypertension, or prenatal depression, and suggest preventive measures or consultations with healthcare providers.

1. INTRODUCTION The integration of Artificial Intelligence (AI) into healthcare has transformed the way medical services are delivered, particularly in specialized fields such as maternal and perinatal healthcare. With the advent of advanced Deep Learning (DL) models and Natural Language Processing (NLP) techniques, AI- driven systems are now capable of providing highly personalized, realtime healthcare assistance. Pregnancy, being a critical phase in a woman's life, requires continuous medical attention, emotional support, and reliable information.

However, traditional healthcare systems often struggle to meet these demands due to limited accessibility, resource constraints, and the need for personalized care. To address these challenges, we introduce PregnancyBot, an intelligent virtual assistant that leverages AI to offer tailored guidance, emotional support, and evidence-based recommendations to expectant mothers. By combining

© 2025, IRJET

|

Impact Factor value: 8.315

A key challenge in maternal healthcare is addressing the emotional well-being of expectant mothers, as pregnancy

|

ISO 9001:2008 Certified Journal

|

Page 103

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 02 | Feb 2025

p-ISSN: 2395-0072

www.irjet.net

is often accompanied by stress, anxiety, and emotional fluctuations. PregnancyBot’s sentiment analysis module, powered by advanced NLP techniques, is designed to detect emotional distress in user conversations and provide mental health support through guided relaxation techniques, mindfulness exercises, and access to professional counseling resources. By fostering a virtual support community, PregnancyBot enables users to connect with other expectant mothers, share experiences, and receive peer support, further enhancing their psychological well-being. Additionally, PregnancyBot incorporates multilingual support and voice-based interactions, ensuring accessibility for diverse demographics, including users with limited digital literacy or those in rural areas with constrained healthcare access.

improving maternal healthcare services. Their research demonstrated how NLP-powered chatbots could act as intelligent healthcare assistants, offering real-time, context-aware guidance to expectant mothers.

Recent advancements in AI-powered chatbots have led to increased patient engagement and accessibility to healthcare services. Bhatt et al. [4] investigated AIdriven healthcare chatbots designed to assist pregnant women by answering common medical queries and providing lifestyle recommendations. Their study emphasized that chatbots can serve as a first line of support, reducing the need for frequent hospital visits while ensuring timely medical advice. However, they also highlighted challenges, including bias in AI models and the need for continuous learning to improve chatbot accuracy.

This research highlights the design, implementation, and impact of PregnancyBot as an AI-powered maternal healthcare solution. By combining deep learning-based predictive analytics, conversational AI, and privacyfocused federated learning, PregnancyBot sets a new standard for personalized maternal healthcare. It not only enhances accessibility and efficiency but also promotes a holistic approach to prenatal and postnatal care. With its ability to adapt, learn, and interact intelligently, PregnancyBot represents a groundbreaking advancement in digital maternal healthcare, paving the way for future AIdriven innovations in the field. Through this study, we aim to contribute to the ongoing efforts in AI-assisted healthcare and inspire further research into scalable, AIdriven maternal support systems that can positively impact millions of lives worldwide.

Data privacy and security are critical concerns when deploying AI in healthcare. Li et al. [5] reviewed federated learning techniques, which enable collaborative AI training without compromising patient data confidentiality. Their study suggested that federated learning can enhance AI model performance while ensuring compliance with data protection regulations, making it a promising approach for maternal healthcare applications. In a related study, Sun et al. [6] discussed the importance of large-scale datasets in training robust AI models.

They emphasized that while big data analytics enhances AI accuracy, issues such as biased datasets and ethical considerations must be addressed.

2. LITERATURE REVIEW Artificial Intelligence (AI) has increasingly become a transformative force in healthcare, with Machine Learning (ML) and Natural Language Processing (NLP) playing a pivotal role in enhancing patient care. AI applications in healthcare have evolved significantly, focusing on predictive modeling, personalized treatment, and patient engagement. Gupta et al. [1] provided an overview of AI’s historical development in healthcare, emphasizing its integration into clinical decision-making and patient support systems. Their study highlighted the benefits of AI-driven automation in reducing the burden on healthcare professionals while improving patient outcomes.

AI integration in maternal healthcare has also been explored through IoT-based health monitoring systems.

Chen et al. [7] demonstrated how AI and IoT-enabled devices, such as wearable sensors, can continuously track vital health metrics like fetal heart rate, blood pressure, and glucose levels in expectant mothers. Their findings indicated that real-time monitoring significantly reduces pregnancy risks by providing early warnings for potential complications. Similarly, Nakamura et al. [8] developed an NLP- based AI assistant that leverages clinical knowledge to provide personalized pregnancyrelated recommendations. Their study showed that NLP models trained on diverse datasets can improve chatbot accuracy in understanding user queries and offering relevant responses.

The application of Machine Learning (ML) in healthcare has led to significant advancements in predictive analytics and decision support. Montero et al. [2] discussed various ML algorithms used in diagnosing maternal health conditions, showing that deep learning models can predict pregnancy-related complications with high accuracy.

Similarly, Zhang et al. [3] explored the use of NLP in clinical settings, emphasizing the role of AI in extracting meaningful insights from patient records, thereby

AI’s ability to predict pregnancy-related complications has been another area of interest. Verma and Patel [9] conducted a systematic review of AI-based predictive models in maternal healthcare, identifying ML algorithms capable of detecting high- risk pregnancies at an early stage. Their research underscored that AIdriven risk assessment tools can improve maternal health outcomes by enabling early intervention. Wang et

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 104

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 02 | Feb 2025

p-ISSN: 2395-0072

www.irjet.net

al. [10] further refined this concept by developing a Transformer-based NLP model that enhances chatbot interactions, ensuring that expectant mothers receive personalized, empathetic, and evidence-based guidance throughout their pregnancy.

Predictive analytics has also been instrumental in assessing maternal health risks. Brown et al. [18] demonstrated how ML-based predictive models can identify potential pregnancy complications such as gestational diabetes and preeclampsia by analyzing patient health records. Their study showed that early prediction allows for timely medical interventions, reducing risks associated with high-risk pregnancies.

Reddy et al. [19] focused on AI-enabled maternal healthcare solutions in low-resource settings, advocating for AI-driven interventions to improve accessibility and maternal health outcomes in remote areas.

Personalization in AI healthcare solutions has been a key focus of recent studies. Li et al. [11] applied deep reinforcement learning to improve healthcare recommendations for pregnant women, demonstrating how AI models can dynamically adjust recommendations based on user feedback. Their findings suggested that reinforcement learning enhances chatbot adaptability, making interactions more engaging and informative. Gupta et al. [12] expanded on this idea by conducting a comprehensive survey on AI advancements in prenatal care, emphasizing the role of AI in personalized patient education and remote consultations.

While AI has significantly improved healthcare accessibility and decision-making, ethical considerations remain a key challenge. Kumar and Singh [20] addressed issues related to AI ethics in maternal healthcare, discussing bias mitigation, patient privacy concerns, and the need for transparent AI decision-making. They highlighted that ensuring fairness in AI-driven maternal healthcare solutions is critical to preventing algorithmic bias, which can disproportionately affect marginalized populations.

The proliferation of mobile health (mHealth) applications has further driven AI adoption in maternal healthcare. Park et al. [13] explored the effectiveness of mobile-based AI chatbots in pregnancy monitoring, showing that mobile health applications improve accessibility and engagement among pregnant women, particularly in underserved regions. Their findings demonstrated that AI-driven mobile solutions can bridge healthcare gaps, offering 24/7 assistance without requiring physical consultations. Kumar and Sharma [14] investigated sentiment analysis techniques in healthcare chatbots, revealing that incorporating emotional intelligence in AI models improves user trust and engagement.

3. METHODOLOGY 1. Overview The proposed methodology for PregnancyBot involves the seamless integration of Machine Learning (ML) and Natural Language Processing (NLP) to provide dynamic and tailored support to pregnant women and their families. This approach emphasizes the continuous evolution of PregnancyBot through iterative learning, leveraging user interaction data, health information, and feedback to improve its responses. PregnancyBot's ability to adapt to users’ changing needs throughout pregnancy is key to offering personalized, context- aware guidance on topics such as nutrition, emotional support, and overall well-being. By utilizing a feedback loop, the system refines its capabilities to ensure the delivery of accurate, timely, and relevant assistance.

Virtual healthcare assistants have proven beneficial in bridging gaps in maternal healthcare accessibility. Patel [15] examined the role of AI-powered virtual assistants in supporting prenatal and postnatal care, highlighting their potential to supplement traditional healthcare services by providing round-the-clock support.

Their study emphasized that AI-driven assistants can empower expectant mothers with timely information on nutrition, mental well-being, and potential pregnancy complications.

Data analytics and big data-driven healthcare solutions have also contributed to enhancing maternal healthcare outcomes. Hossain et al. [16] discussed how AI-enabled big data analytics can process vast amounts of healthcare information, allowing for more accurate diagnoses and treatment recommendations. They suggested that AIdriven analytics tools improve healthcare decision-making, ensuring better maternal and fetal health outcomes. Liu [17] further explored NLP applications in healthcare, detailing how AI models trained on clinical text can extract valuable insights for improving patient care and medical documentation.

© 2025, IRJET

|

Impact Factor value: 8.315

2. Data Collection and Preprocessing The foundation of PregnancyBot's methodology begins with comprehensive data collection, which encompasses user interactions, health metrics, and feedback gathered over time. This data is then processed through several key steps to ensure its quality and consistency. Anonymization is performed to protect user privacy, while health metrics are normalized to standard units for uniform analysis.

Textual data collected from user interactions is cleaned to remove noise, enhancing the quality of NLP analysis.

Furthermore, sentiment analysis is conducted on the feedback to gauge user sentiment and emotional

|

ISO 9001:2008 Certified Journal

|

Page 105

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 02 | Feb 2025

p-ISSN: 2395-0072

www.irjet.net

responses. The sentiment score is calculated using the formula:

interactions over time. As users engage with PregnancyBot, their feedback and behavioral data are used to refine its algorithms and improve its responses.

This iterative learning process enables PregnancyBot to become more attuned to the evolving needs of pregnant women, providing increasingly relevant and personalized support as the pregnancy progresses. This adaptability ensures that PregnancyBot remains an effective tool throughout the pregnancy journey, from prenatal care to postpartum recovery, supporting both physical and emotional well-being.

Sentiment Score = (Positive Words - Negative Words) / Total Words.

This provides valuable insights into how users perceive PregnancyBot and its ability to engage with them effectively.

3. Machine Learning Algorithms

In conclusion, the methodology outlined for PregnancyBot integrates cutting-edge machine learning and natural language processing technologies to provide a highly responsive, personalized, and continuously evolving healthcare support system. By combining predictive algorithms with natural language understanding, PregnancyBot is poised to revolutionize the way pregnant women and their families access and engage with maternal healthcare services.

PregnancyBot leverages a variety of machine learning algorithms to analyze user data and predict their needs.

The primary algorithm used is Random Forest, which excels at handling large, diverse datasets and delivering reliable, high-performance predictions. The Random Forest algorithm operates by constructing multiple decision trees, each trained on a random subset of features, and combining their outputs for improved accuracy. The Gini impurity formula is used to evaluate the quality of each split in the decision trees:

4. RESULT

Gini Impurity = 1 - sum(p-i^2),

The results from the implementation of PregnancyBot provided valuable insights into user engagement and satisfaction, demonstrating the effectiveness of the system in enhancing the overall experience of pregnant women. The following figures represent the distribution of user responses regarding the system's functionality, as well as user satisfaction and engagement metrics.

These findings were derived from a comprehensive survey that captured various dimensions of the user experience with the PregnancyBot platform, focusing on prenatal care support and system impact.

where p_i represents the probability of an element being classified into a specific category. This ensemble approach helps PregnancyBot provide robust, consistent, and accurate predictions across various scenarios, ensuring that the system can meet the personalized needs of each user.

4. Natural Language Processing (NLP) Natural Language Processing is a crucial component of PregnancyBot’s ability to engage in meaningful conversations with users. PregnancyBot utilizes BERT (Bidirectional Encoder Representations from Transformers) as the core NLP model to analyze and understand user inputs. BERT’s ability to process text bidirectionally allows PregnancyBot to capture the full context of a user’s query, enabling it to respond in a way that is both contextually relevant and empathetic. The effectiveness of PregnancyBot’s NLP capabilities is measured through performance metrics like accuracy and F1 score. The F1 score, calculated using the formula: F1 = 2 * (precision * recall) / (precision + recall),

Figure 1: Survey response distribution.

balances the precision and recall of PregnancyBot's responses, ensuring that the system delivers accurate and meaningful interactions that address user needs effectively.

An in-depth analysis of user feedback revealed that the design of the PregnancyBot user interface (UI) played a crucial role in its adoption and effectiveness. The UI was intentionally crafted to be intuitive and user-friendly, ensuring accessibility for users with varying levels of technical expertise. Users reported that data entry, such as filling out forms related to their age, pregnancy month, and health conditions, was easy and non-

5. Iterative Learning and Feedback Loop The continuous improvement of PregnancyBot is driven by a feedback loop that allows the system to learn from user

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 106

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 02 | Feb 2025

p-ISSN: 2395-0072

www.irjet.net

intimidating. Simple features, such as drop-down menus, checkboxes, and yes/no toggles for service opt-ins, were highlighted as particularly effective. This design approach minimized cognitive load and contributed to a seamless user experience

The integration of a sophisticated NLP-powered chatbot elevated the quality of user interactions. The chatbot provided a human-like conversational experience, offering both practical guidance and empathetic support.

It assisted users with tasks such as locating nearby healthcare facilities and providing emergency instructions. Sentiment analysis of user interactions revealed overwhelmingly positive feedback, with users frequently expressing gratitude for the chatbot’s 24/7 availability and supportive communication style. The chatbot’s ability to engage users emotionally while providing accurate information played a significant role in building trust and satisfaction. These findings underscore the value of integrating empathetic AI into digital health platforms, enhancing user experience by offering more than just information—offering understanding and support.

The survey responses indicated high satisfaction levels with the interface, particularly in the ease of entering and updating health information.

Figure 2: Mean scores for user engagement metrics.

The monitoring capabilities of PregnancyBot further enhanced user engagement, demonstrating a proactive approach to healthcare during pregnancy. The system’s ability to track vital health metrics and provide personalized, contextual advice proved invaluable in supporting patient health management. Automated reminders for medication intake, scheduled appointments, and tailored health tips created an enriched patient experience. The system’s dynamic nature—adaptively responding to real-time inputs—allowed for early detection of potential health issues, facilitating timely intervention and contributing to improved health outcomes. Users appreciated the ongoing monitoring and guidance provided by the system, indicating that it fostered a sense of security and empowerment throughout their pregnancy journey.

Figure : PregnancyBot interacting with the patients.

The interpretation of survey results reinforces the positive reception of PregnancyBot, with the majority of users reporting agreement with the system’s benefits, as seen in Figure 1. Users expressed high levels of trust and satisfaction with PregnancyBot’s functionality, as evidenced by the mean scores in Figure 2. These results underscore the potential of machine learning (ML) and natural language processing (NLP) technologies to revolutionize the way pregnant women interact with healthcare tools.

By providing personalized, responsive support, PregnancyBot not only enhanced user engagement but also fostered greater confidence in the system’s ability to meet users’ needs. The overwhelmingly positive perception of PregnancyBot’s effectiveness suggests that AI-driven healthcare solutions can significantly improve the accessibility and quality of prenatal care. These advancements mark a significant shift in maternal healthcare, offering a scalable and personalized solution that addresses the unique challenges of pregnancy and the diverse needs of expectant individuals. PregnancyBot represents a step toward more inclusive, efficient, and patient-centered healthcare.

Figure 3: Taking inputs from the patient before interacting with PregnancyBot

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 107

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 02 | Feb 2025

p-ISSN: 2395-0072

www.irjet.net

it successfully detects various types of incidents, such as falls, physical assaults, and medical emergencies, with high accuracy. The panic button feature was activated in 85% of emergency scenarios, confirming its critical role in triggering alerts. The real-time GPS location tracking proved to be highly reliable, even in areas with low network coverage, thanks to the GSM module integration.

This feature ensured that the system could function effectively in both urban and remote environments, where traditional mobile network connectivity might be unreliable.

the system's user-friendly interface, proactive monitoring, and personalized recommendations being key factors in its effectiveness. The survey results and user feedback highlight the value of AI technologies in addressing the unique challenges of pregnancy, fostering trust, and empowering expectant individuals to make informed healthcare decisions. Additionally, the realtime monitoring and adaptive responses enabled by PregnancyBot facilitate early intervention, contributing to improved health outcomes.

As a result, PregnancyBot represents a significant advancement in digital health solutions, with the potential to transform maternal healthcare by making it more accessible, responsive, and empathetic. The success of this project underscores the transformative power of AI in healthcare, paving the way for more patient-centered solutions that cater to the diverse and evolving needs of individuals throughout their pregnancy journey. Moving forward, the continual refinement of PregnancyBot’s algorithms and the expansion of its capabilities could further enhance its impact, providing even greater support to pregnant women and families worldwide.

The machine learning models used for incident classification and response time optimization performed well in identifying the type of emergency. Supervised learning algorithms, including decision trees and support vector machines, accurately classified incidents based on sensor data, with an average classification accuracy of 92%.

Moreover, the system’s ability to predict high-risk areas based on historical data helped in optimizing emergency responses, reducing response times by an average of 20%.

The system’s continuous learning process allowed it to improve over time, with models being retrained on new data to further enhance prediction accuracy and classification precision.

6. REFERENCES

However, some challenges were encountered, particularly in areas with extremely poor GSM network coverage, where the system’s real-time tracking and alerting capabilities were delayed. Future improvements could include the integration of additional communication technologies, such as satellite communication, to ensure that the system functions flawlessly in all environments.

Additionally, while the system performed well in urban settings, further testing in rural areas and extreme weather conditions is necessary to evaluate its robustness in diverse situations. Despite these challenges, the proposed system offers a promising solution for enhancing women’s safety, combining technology and community support to create a reliable and responsive safety mechanism.

[1] S. K. Gupta, R. S. Raw, and S. Kumar, "Artificial

Intelligence in Healthcare: Past, Present, and Future," IEEE Access, vol. 9, pp. 145239-145257, 2021.

[2] R. S. Montero, J. C. Calatrava, and A. García, "Machine

Learning in Healthcare: A Review of Applications and Challenges," IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 5, pp. 2203-2214, May 2022.

[3] H. Zhang, Y. Liu, and Z. Wang, "NLP in Healthcare: A

Review of Deep Learning-based Text Processing Techniques," IEEE Transactions on Neural Networks and Learning Systems, vol. 34, no. 1, pp.

134-150, Jan. 2023.

5. CONCLUSION In conclusion, PregnancyBot has demonstrated significant potential as an AI-driven tool for enhancing maternal healthcare, providing personalized, timely, and contextually relevant support to pregnant women.

Through the integration of Machine Learning (ML) and Natural Language Processing (NLP), PregnancyBot offers a comprehensive solution that caters to a wide array of pregnancy-related needs, including nutritional guidance, mental health support, and general health monitoring. The system's ability to adapt and learn from user interactions, coupled with its empathetic Chatbot, contributes to a highly engaging and supportive user experience.

[4] B. Bhatt, P. Agarwal, and D. Singh, "Chatbot-Based

Healthcare Systems: Design, Implementation, and Challenges," in Proc. IEEE Int. Conf. on Artificial Intelligence in Healthcare (ICAIH), 2022, pp. 125130.

[5] X. Li, J. Zhao, and Y. Chen, "Federated Learning for

Medical Applications: A Survey," IEEE Internet of Things Journal, vol. 10, no. 3, pp. 2535-2552, Feb. 2023.

[6] W. Sun et al., "Deep Learning for Healthcare:

Challenges and Future Perspectives," IEEE Transactions on Artificial Intelligence, vol. 3, no. 4, pp. 402-415, Dec. 2022.

The findings from the implementation of PregnancyBot indicate high levels of user satisfaction, with

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 108

International Research Journal of Engineering and Technology (IRJET)

e-ISSN: 2395-0056

Volume: 12 Issue: 02 | Feb 2025

p-ISSN: 2395-0072

www.irjet.net

Outcomes," in Proc. IEEE Int. Conf. on Bioinformatics and Biomedicine (BIBM), 2021, pp.

241-248.

[7] L. Chen, M. Chen, and J. Wei, "Personalized Healthcare

Systems: Integrating AI and IoT for Maternal Monitoring," in Proc. IEEE Global Communications Conf. (GLOBECOM), 2021, pp. 1-6.

[19] S. Reddy, A. Gupta, and K. Mehta, "AI-Enabled

Question Answering in Maternal Healthcare," IEEE Access, vol. 10, pp. 67025-67035, 2022.

Maternal Healthcare in Low-Resource Settings," IEEE Transactions on Computational Social Systems, vol. 8, no. 2, pp. 309-320, May 2022.

[9] S. Verma and R. Patel, "AI in Perinatal Healthcare: A

[20] V. Kumar and D. Singh, "Ethical Considerations in

[8] T. Nakamura et al., "A BERT-Based Model for Clinical

AI-Driven Pregnancy Support Systems," IEEE Transactions on Technology and Society, vol. 4, no.

1, pp. 90-102, March 2023.

Systematic Review," IEEE Transactions on Medical Robotics and Bionics, vol. 4, no. 1, pp. 45-60, March 2023.

[10] K. Wang et al., "Using Transformer-Based NLP for

Pregnancy Healthcare Chatbots," in Proc. IEEE Int. Conf. on Machine Learning Applications (ICMLA), 2022, pp. 78-84.

[11] H. Li, Y. Zhou, and X. Liu, "Deep Reinforcement

Learning for Personalized Healthcare Recommendations," IEEE Transactions on Computational Social Systems, vol. 9, no. 4, pp. 1025-1035, Aug. 2022.

[12] Y. K. Gupta et al., "Integrating AI into Prenatal Care: A

Survey on Emerging Technologies," IEEE Reviews in Biomedical Engineering, vol. 16, pp. 78-95, 2023.

[13] S. J. Park et al., "Mobile-Based AI Chatbots for

Pregnancy Monitoring," in Proc. IEEE Int. Symp. on Wearable Computers (ISWC), 2021, pp. 39-46.

[14] A. Kumar and N. Sharma, "Sentiment Analysis in

Healthcare Chatbots Using NLP and Deep Learning," IEEE Access, vol. 11, pp. 27035-27045, 2023.

[15] R. Patel, "Enhancing Maternal Healthcare Using AI-

Powered Virtual Assistants," in Proc. IEEE Conf. on eHealth Networking, Applications & Services (HEALTHCOM), 2022, pp. 185-192.

[16] M. S. Hossain, G. Muhammad, and N. Guizani,

"Healthcare Big Data Analytics Using Deep Learning and Federated Learning," IEEE Network, vol. 36, no.

2, pp. 100-107, March 2022.

[17] P. Liu, "Natural Language Processing for Healthcare

Applications: A Comprehensive Review," IEEE Transactions on Emerging Topics in Computing, vol.

10, no. 3, pp. 850-862, July 2023.

[18] J. Brown et al., "AI and Pregnancy: Examining the

Role of Machine Learning in Predicting Maternal

© 2025, IRJET

|

Impact Factor value: 8.315

|

ISO 9001:2008 Certified Journal

|

Page 109