Role of Chatbots and Deep Learning in Predicting Heart Attack Risk

Ashvini Kumar Mishra

Role of Chatbots and Deep Learning in Predicting Heart Attack Risk

Authors: Ashvini Kumar Mishra

Published: 2025-03-05 23:46:31.613

Abstract

In today's era of increasing reliance on mobile devices, chatbots play a vital role due to their simplicity and accessibility. The COVID-19 pandemic has further highlighted the insufficiency of healthcare resources, emphasizing the need for scalable digital solutions. This paper presents an application that leverages deep learning to assist with online disease diagnosis via a chatbot interface. The study focuses on predicting an individual’s susceptibility to heart attacks based on specific health indicators. Using a robust dataset, a deep learning model was developed to analyse key features and accurately assess the risk of cardiac events. The model was then integrated into a chatbot, allowing users to access personalized health insights in real-time. By combining advanced machine learning techniques with an intuitive conversational interface, the proposed system aims to enhance early detection and preventive care. The application is designed to reduce the burden on healthcare systems while empowering individuals with critical health information in a user-friendly format. This approach demonstrates the potential of integrating artificial intelligence with conversational platforms to address pressing health challenges effectively.

Keywords

Chatbot, Deep Learning, Heart Attack Prediction, Disease Diagnosis, Mobile Health Applications

References

Eleonora Pantano, Gabriele Pizzi, Forecasting artificial intelligence on online customer assistance: Evidence from chatbot patents analysis, Journal of Retailing and Consumer Services, Volume 55, 2020, 102096, ISSN 0969-6989,
Alaa A. Abd-alrazaq, Mohannad Alajlani, Ali Abdallah Alalwan, Bridgette M. Bewick, Peter Gardner, Mowafa Househ,An overview of the features of chatbots in mental health: A scoping review, International Journal of Medical Informatics, Volume 132, 2019, 103978, ISSN 1386-5056,
Vijayaraghavan V., Jack Brian Cooper, Rian Leevinson J., Algorithm Inspection for Chatbot Performance Evaluation, Procedia Computer Science, Volume 171, 2020, Pages 2267-2274, ISSN 1877-0509, [4] Dharun Lingam Kasilingam, Understanding the attitude and intention to use smartphone chatbots for shopping, Technology in Society, Volume 62, 2020 101280, ISSN 0160-791X.
Aleksandra Przegalinska, Leon Ciechanowski, Anna Stroz, Peter Gloor, Grzegorz Mazurek, In bot we trust: A new methodology of chatbot performance measures, Business Horizons,.
Pavel Smutny, Petra Schreiberova, Chatbots for learning: A review of educational chatbots for the Facebook Messenger, Computers & Education, Volume 151, 2020, 103862, ISSN 0360-1315, .Mohammad Nuruzzaman, Omar Khadeer Hussain, IntelliBot: A Dialogue-based chatbot for the insurance industry, Knowledge-Based Systems, Volume 196, 2020, 105810, ISSN 0950-7051.
Ben Sheehan, Hyun Seung Jin, Udo Gottlieb, Customer service chatbots: Anthropomorphism and adoption, Journal of Business Research, Volume 115, 2020, Pages 14-24, ISSN 0148-2963.
Qiyue Wang, Wenhua Jiao, Peng Wang, YuMing Zhang, A tutorial on deep learning-based data analytics in manufacturing through a welding case study, Journal of Manufacturing Processes, 2020, , ISSN 1526-6125.
Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828. https://doi.org/10.1109/TPAMI.2013.50
Esteva, A., Robicquet, A., Ramsundar, B., Kuleshov, V., DePristo, M., Chou, K., ... & Dean, J. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24–29. https://doi.org/10.1038/s41591-018-0316-z
Grewal, P. S., & Dhaliwal, J. K. (2022). Chatbots as tools for healthcare service delivery: A review of applications and challenges. Healthcare Technology Letters, 9(3), 123–129. https://doi.org/10.1049/htl.2022.0012
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., ... & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present, and future. Stroke and Vascular Neurology, 2(4), 230–243. https://doi.org/10.1136/svn-2017-000101
Luo, W., Phung, D., Tran, T., Gupta, S., Rana, S., Karmakar, C., ... & Venkatesh, S. (2016). Guidelines for developing and reporting machine learning predictive models in biomedical research: A multidisciplinary view. Journal of Medical Internet Research, 18(12), e323. https://doi.org/10.2196/jmir.5870
Miner, A. S., Laranjo, L., & Kocaballi, A. B. (2020). Chatbots in the fight against the COVID-19 pandemic. Nature Digital Medicine, 3(1), 1–3. https://doi.org/10.1038/s41746-020-0280-0
Nguyen, T. T., Nguyen, T. H., & Tran, T. N. (2021). A deep learning-based approach for heart disease diagnosis using health records and real-time data. Expert Systems with Applications, 173, 114728. https://doi.org/10.1016/j.eswa.2021.114728
Ning Wang, Yuanyuan Wang, Meng Joo Er, Review on deep learning techniques for marine object recognition: Architectures and algorithms, Control Engineering Practice, 2020, 104458, ISSN 0967-0661.

Download PDF

How to Cite

Ashvini Kumar Mishra, (2025-03-05 23:46:31.613). Role of Chatbots and Deep Learning in Predicting Heart Attack Risk. JANOLI International Journal of Computer Science and Engineering , Volume rn1ql9uo4BygpjFCAoIa, Issue 1.

ISSN: A/F

JANOLI International Journal of
Computer Science and Engineering ( JIJCSE )