Abstract
Artificial Intelligence is becoming an important Computer Science field that is being leveraged periodically, to be collaborated with everyday applications. Health care is a major part of our lives and it is inevitable. It is not always easy to await a doctor’s appointment to find out what the health problem or disease could be, for various reasons. The period of COVID-19 has especially been challenging in having access to doctors and hospital visits; despite minor health issues, people refrain from visiting medical institutions. chatbots have progressed to become quite handy in the medical industry for various purposes––predicting diseases, medications, pathology queries, and even just for general medical awareness at much lesser cost and resources. A conversational chatbot, like the one aimed to be created here, is a prototypical model for providing users a pre-diagnosis based on symptoms and concerns mentioned by the user. The model utilizes NLP and Neural Networks together, and Decision tree classifiers separately for two different ways of diagnosis. This can, therefore, assist users to get an initial idea and how to proceed about it further.
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References
Anil Kumar, S., Vamsi Krishna, C., Nikhila Reddy, P., Rohith Kumar Reddy, B., Jeena Jacob, I.: Self-diagnosing health care chatbot using machine learning. Int. J. Adv. Sci. Technol. 29(05), 9323–9330 2020. http://sersc.org/journals/index.php/IJAST/article/view/19027
Rarhi, K., Bhattacharya, A., Mishra, A., Mandal, K.: Automated medical chatbot. SSRN Electron. J. (2017). https://doi.org/10.2139/ssrn.3090881
Dharwadkar, R., Deshpande, N.A.: A medical ChatBot. Int. J. Comput. Trends Technol (IJCTT) 60(1) (2018)
Amato, F., Marrone, S., Moscato, V., Piantadosi, G., Picariello, A., Sansone, C.: Chatbots meet eHealth: automatizing healthcare. In: Impedovo, D., Pirlo, G., (eds) Workshop on Artificial Intelligence with Application in Health, Bari, Italy, WAIAH@AI*IA (2017)
Madhu, D., Jain, C.J.N., Sebastain, E., Shaji, S., Ajayakumar, A.: A novel approach for medical assistance using trained chatbot. In: 2017 International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, pp. 243–246 2017. https://doi.org/10.1109/icicct.2017.7975195
Divya, S., Indumathi, V., Ishwarya, S., Priyasankari, M., Kalpana Devi, S.: Survey on medical self-diagnosis chatbot for accurate analysis using artificial intelligence. Int J Trend Res Dev 5(2) (2018). ISSN: 2394-9333
Mathew, R.B., Varghese, S., Joy, S.E., Alex, S.S.: Chatbot for disease prediction and treatment recommendation using machine learning. In: 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India, pp. 851–856 (2019). https://doi.org/10.1109/icoei.2019.8862707
Shangrapawar, A., Ravekar, A., Kale, S., Kumari, N., Shende, A., Taklikar, P.: Artificial intelligence based healthcare chatbot system. Int. Res. J. Eng. Technol. (IRJET) 07(02) (2020)
Ayanouz, S., Abdelhakim, A.B., Benhmed, M.: A smart chatbot architecture based NLP and machine learning for health care assistance. In: The Fifth International Conference on Smart City Applications (2020). https://doi.org/10.1145/3386723.3387897
Battineni, G., Chintalapudi, N., Amenta, F.: AI chatbot design during an epidemic like the novel coronavirus. Healthcare 8(2), 154 (2020). https://doi.org/10.3390/healthcare8020154
Athota, L., Shukla, V.K., Pandey, N., Rana, A.:. Chatbot for healthcare system using artificial intelligence. In: 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, India, pp. 619–622 (2020). https://doi.org/10.1109/icrito48877.2020.9197833
Omoregbe, N.A.I., Ndaman, I.O., Misra, S., Abayomi-Alli, O.O., Damaševičius, R.: Text messaging-based medical diagnosis using natural language processing and fuzzy logic. J. Healthc. Eng. 2020, 14, Article ID 8839524 (2020). https://doi.org/10.1155/2020/8839524
Dolianiti, F., Tsoupouroglou, I., Antoniou, P., Konstantinidis, S., Anastasiades, S., Bamidis, P.: Chatbots in healthcare curricula: the case of a conversational virtual patient. In: Frasson, C., Bamidis, P., Vlamos, P., (eds) Brain Function Assessment in Learning. BFAL Lecture Notes in Computer Science, vol. 12462. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60735-7_15
Chen, J., Agbodike, O., Wang, L.: Memory-based deep neural attention (mDNA) for cognitive multi-turn response retrieval in task-oriented chatbots. Appl. Sci. 10(17), 5819 (2020). https://doi.org/10.3390/app10175819
Patil, P.: Disease Symptom Prediction. Kaggle (2020). https://www.kaggle.com/itachi9604/disease-symptom-description-dataset?select=dataset.csv
A to Z list of common illnesses and conditions | NHS inform. (n.d.). NHS Inform. Accessed 10 Dec 2020. https://www.nhsinform.scot/illnesses-and-conditions/a-to-z
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Kurup, G., Shetty, S.D. (2022). AI Conversational Chatbot for Primary Healthcare Diagnosis Using Natural Language Processing and Deep Learning. In: Das, A.K., Nayak, J., Naik, B., Dutta, S., Pelusi, D. (eds) Computational Intelligence in Pattern Recognition . Advances in Intelligent Systems and Computing, vol 1349. Springer, Singapore. https://doi.org/10.1007/978-981-16-2543-5_22
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