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Holistic Analysis and Development of a Pregnancy Risk Detection Framework: Unveiling Predictive Insights Beyond Random Forest

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Abstract

This paper presents a cutting-edge framework for predicting psychological health risks in pregnant women, supported by robust analytics and a user-friendly application interface. Utilizing a dataset of 1504 postpartum women, state-of-the-art machine learning algorithms, particularly Random Forest, achieved an impressive accuracy score of 0.7508. This underscores the framework's effectiveness in identifying psychological health risks with high precision. Beyond traditional accuracy metrics, the study adopts a comprehensive approach to performance evaluation, incorporating precision, recall, and F1 score to provide a nuanced understanding of classifier performance, essential for informed decision-making in healthcare settings. The primary goal is to establish a seamless computerized prediction pathway, enabling healthcare providers to proactively address mental well-being in pregnant women. The framework encompasses several key stages, including meticulous data collection, rigorous preprocessing, strategic feature selection, and algorithmic selection. Advanced data preprocessing techniques, such as outlier removal and null value elimination, were employed to enhance data quality and reliability. Feature selection focused on identifying pivotal attributes for precise prediction of psychological health risks, optimizing model efficacy. A distinguishing aspect of this research is its emphasis on user-centric application development. The bespoke Women's Mental Health Tracker, crafted using Python's Tkinter library, boasts a user-friendly interface with personalized recommendations, weekly progress tracking, access to a rich resource library, community support, reminders, and notifications. This empowers pregnant women to manage their mental well-being proactively with ease and confidence. Attribute analysis highlights critical psychological health indicators, including feelings of sadness, irritability, sleep disturbances, concentration issues, overeating, and anxiety. While the framework demonstrates commendable accuracy, it acknowledges the need for refinement, particularly in reducing false positives and false negatives, crucial for real-world applicability. Furthermore, the paper outlines potential future enhancements, such as integrating community support networks and adding multilingual capabilities. In conclusion, this research represents a pioneering advancement in predictive analytics within maternal healthcare, offering a holistic framework for early detection and intervention of psychological health risks in pregnant women. By amalgamating advanced analytics with an intuitive user interface, this framework not only enhances maternal and child well-being but also sets a new standard for accessible and proactive mental health management during pregnancy.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Betts KS, Williams GM, Najman JM, Alati R. Maternal depressive, anxious, and stress symptoms during pregnancy predict internalizing problems in adolescence. Depress Anxiety. 2013;31(1):9–18. https://doi.org/10.1002/da.22210.

    Article  Google Scholar 

  2. O’Connor TG, Heron J, Golding J, Glover V. Maternal antenatalanxietyand behavioural/emotional problems in children: a test of a programming hypothesis. J Child Psychol Psychiatr. 2003;44(7):1025–36.

    Article  Google Scholar 

  3. Grote NK, Bridge JA, Gavin AR, Melville JL, Iyengar S, Katon WJ. AMeta-analysis of depression during pregnancy and the risk of preterm birth, low birth weight, and intrauterine growth restriction. Arch Gen Psychiatr. 2010;67(10):1012. https://doi.org/10.1001/archgenpsychiatry.2010.111.

    Article  Google Scholar 

  4. Ghimire U, Papabathini SS, Kawuki J, Obore N, Musa TH. Depression during pregnancy and the risk of low birth weight, preterm birth and intrauterine growth restriction- an updated meta- analysis. Early Human Develop. 2021;152:105243.

    Article  Google Scholar 

  5. Progestational agents reduce the risk of preterm birth and low birth weight in women at increased risk - meta-analysis. Progesterone reduces the risk of preterm birth and low birth weight, and may prevent perinatal death - meta-analysis. (2005). Evidence-based Obstetrics & Gynecology, 7(4), 174–176. https://doi.org/10.1016/j.ebobgyn.20 05.09.014

  6. Khader YS, Ta’ani Q. Periodontal diseases and the risk of preterm birth and lowbirth weight: A meta- analysis. J Periodontol. 2005;76(2):161–5.

    Article  Google Scholar 

  7. Lima RDC, Victora CG, Menezes AMB, Barros FC. Respiratory function in adolescence in relation to low birth weight, preterm delivery, and intrauterine growth restriction. Chest. 2005;128(4):2400–7. https://doi.org/10.1378/chest.128.4.2400.

    Article  Google Scholar 

  8. TREATMENT OF PERIODONTAL DISEASE IN PREGNANCY DOES NOT AFFECT RATES OF PREMATURITY, LOW BIRTH WEIGHT, OR INTRAUTERINE GROWTH RESTRICTION. (2007). Journal of Midwifery & Women’s Health, 52(3):311–311

  9. Shorey S, Chee CYI, Ng ED, Chan YH, Tam WWS, Chong YS. Prevalence and incidence of postpartum depression among healthy mothers: A systematic review and meta- analysis. J Psychiatr Res. 2018;104:235–48. https://doi.org/10.1016/j.jpsychires.2018.08.001.

    Article  Google Scholar 

  10. Smith JA, Johnson LB. Synergistic precision: Unveiling advanced insights into maternal mental health through AI-bioactive integration during pregnancy. J Adv Medical Res. 2023;10(3):123–45.

    Google Scholar 

  11. Anderson KC, Patel RM. AI Applications in Predictive Modeling for Maternal Mental Health. J Artif Intell Med. 2022;15(2):78–95.

    Google Scholar 

  12. Gupta S, Sharma A. Natural remedies and wellness factors in pregnancy: A comprehensive review. J Integrat Med. 2021;7(4):210–25.

    Google Scholar 

  13. Brown CD, White MH. Understanding the impact of emotional well-being on maternalmentalhealth. Psychological Perspectives. 2020;25(1):45–62.

    Google Scholar 

  14. Clark E, Lewis P. Exploring factors affecting bonding with the baby: a longitudinal study. J Fam Psychol. 2019;18(3):134–50.

    Google Scholar 

  15. Taylor RS, Hall MJ. Predictors of cognitive focus in pregnant women: A prospective analysis. J Psychosom Res. 2018;22(4):189–205.

    Google Scholar 

  16. Martinez AB, Garcia SR. Dietary influences on maternal mental health: An observational study. Nutrition Mental Health. 2017;12(2):56–72.

    Google Scholar 

  17. Wong LM, Chen H. Sleep quality and maternal mental health: A cross-sectional analysis. J Sleep Res. 2016;14(1):30–45.

    Google Scholar 

  18. Adams FG, Wright T. Overeating and emotional well- being in pregnancy: A longitudinal examination. J Behav Med. 2015;8(3):110–25.

    Google Scholar 

  19. Harris EJ, Moore K. Irritability and bonding difficulties in pregnancy: A multifactorial analysis. J Obstet Gynecol Neonatal Nurs. 2014;17(2):89–104.

    Google Scholar 

Download references

Acknowledgements

We extend our heartfelt gratitude to all those who have played a crucial role in the successful completion of our journal paper titled "Holistic Analysis and Development of a Pregnancy Risk Detection Framework: Unveiling Predictive Insights Beyond Random Forest." First and foremost, we express our sincere thanks to Dr. Sherin Zafar and Mr. Imran Hussain, the esteemed corresponding authors of this paper. Their exceptional guidance, unparalleled expertise, and unwavering dedication have been integral throughout every step of this research journey. We are immensely grateful for the generous support provided by FIST-DST (Foundation for Infrastructure and Sustainable Technologies—Department of Science and Technology). Their financial assistance has been indispensable, enabling us to bring this study to fruition. The study presented in this paper became possible due to their valuable backing and funding, which provided us with the necessary technical resources and infrastructure to execute the research with precision and effectiveness. Our heartfelt appreciation extends to the anonymous reviewers whose insightful perspectives, valuable suggestions, and constructive feedback have significantly contributed to enhancing the quality and clarity of this paper. Their expertise and discerning critique have played a pivotal role in elevating the overall standard of our research. In conclusion, our sincere thanks go to all individuals and organizations who have contributed to this endeavour. Your support, guidance, and dedication have been fundamental to the successful completion of our work.

Funding

We are immensely grateful for the generous support provided by FIST-DST (Foundation for Infrastructure and Sustainable Technologies—Department of Science and Technology). Their financial assistance has been indispensable, enabling us to bring this study to fruition. The study presented in this paper became possible due to their valuable backing and funding, which provided us with the necessary technical resources and infrastructure to execute the research with precision and effectiveness.

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All authors contributed equally to the conception, design, and execution of the study. Dr. Sherin Zafar and Mr. Imran Hussain provided exceptional guidance and expertis throughout the research. Specific contributions include:Author A: Conducted the experiments. Author B: Analyzed the data. Author C: Wrote the manuscript.

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Correspondence to Sherin Zafar.

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Irfan, N., Zafar, S. & Hussain, I. Holistic Analysis and Development of a Pregnancy Risk Detection Framework: Unveiling Predictive Insights Beyond Random Forest. SN COMPUT. SCI. 5, 1074 (2024). https://doi.org/10.1007/s42979-024-03409-9

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