Healthcare Chat Bot for Identifying Diseases and Providing Referrals Using Machine Learning

Abstract

Healthcare chatbots have the potential to revolutionize healthcare by providing accessible, affordable, and personalized medical advice and support to individuals. In this research paper, we propose a healthcare chatbot system that uses machine learning algorithms to identify diseases and provide appropriate referrals to patients. The proposed chatbot is designed to interact with patients via natural language processing (NLP) and answer their questions related to their symptoms and other relevant factors. The system uses a deep learning-based approach to analyze patient data and provide accurate and personalized recommendations. The proposed chatbot system was evaluated using a dataset of medical records from patients with various diseases such as dengue, influenza, nail diseases. The evaluation results showed that the proposed system achieved high accuracy in identifying diseases and providing referrals. Furthermore, the system was able to provide personalized recommendations based on patients' unique symptoms and other relevant factors. The proposed healthcare chatbot system has the potential to improve healthcare delivery by providing quick and personalized medical advice and support to patients. The system's ability to identify diseases like nail diseases, acne diseases, covid-19, dengue, influenza and provide first aid using machine learning algorithms can help healthcare providers diagnose diseases at an early stage and provide timely and effective treatment to patients. Additionally, the proposed chatbot system can be easily integrated into existing healthcare systems, making it accessible to a broader population.

Country : Sri Lanka

1 Pamudu Ratnayake2 Hansani Bandara3 Oshini Cooray4 Chamathka Ariyarathna5 Suriyaa Kumari6 Ravi Supunya

  1. Undergraduate, Faculty of Computing, Sri Lanka Institute of Information Technology, Western Province, Sri Lanka
  2. Undergraduate, Faculty of Computing, Sri Lanka Institute of Information Technology, Western Province, Sri Lanka
  3. Undergraduate, Faculty of Computing, Sri Lanka Institute of Information Technology, Western Province, Sri Lanka
  4. Undergraduate, Faculty of Computing, Sri Lanka Institute of Information Technology, Western Province, Sri Lanka
  5. Lecturer, Faculty of Computing, Sri Lanka Institute of Information Technology, Western Province, Sri Lanka
  6. Lecturer, Faculty of Computing, Sri Lanka Institute of Information Technology, Western Province, Sri Lanka

IRJIET, Volume 7, Issue 6, June 2023 pp. 115-122

doi.org/10.47001/IRJIET/2023.706018

References

  1. M. A. Suva1, A. M. Patel, N. Sharma, C. Bhattacharya and R. K. Mangi, "A Brief Review on Acne Vulgaris: Pathogenesis, Diagnosis and Treatment," 2014.
  2. N. Razavian and D. Sontag, "TEMPORAL CONVOLUTIONAL NEURAL NETWORKS," 2016.
  3. G. Maroni, M. Ermidoro, F. Previdi and G. Bigini, "Automated detection, extraction and counting of acne lesions for automatic evaluation and tracking of acne severity," 2017.
  4. X. Shen, J. Zhang, C. Yan and H. Zhou, "An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network," 2018.
  5. T. Chantharaphaichi, B. Uyyanonvara, C. Sinthanayothin and A. Nishihara, "Automatic acne detection for medical treatment," 2015.
  6. Kaur, H., & Singh, S. (2020). Diagnosis of dengue fever using decision tree algorithm. International Journal of Advanced Science and Technology, 29(4), 1314-1324.
  7. Hassan, A., Rehman, A., Kiani, R., & Khan, A. U. (2020). Deep learning based COVID-19 diagnosis from chest X-ray images. Journal of infection and public health, 13(5), 710-714.
  8. El-Melegy, M. T., El-Bialy, M. S., & Ramadan, R. A. (2017). Liver diseases diagnosis using support vector machine. International Journal of Medical Informatics, 101, 1-10.
  9. Amruthnath, N., Ravi, V., & Kumar, V. (2021). Diagnosis of pneumonia using natural language processing and deep learning. In 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence) (pp. 420-424). IEEE.
  10. Bhattacharya, S., Mukherjee, A., & Kumar, M. (2019). An intelligent diagnostic decision support system for dengue fever. International Journal of Medical Engineering and Informatics, 11(4), 340-361.
  11. Jain, G., Mittal, D., & Thakur, A. (2020). Detection of COVID-19 using X-ray images and deep learning models. Journal of Biomolecular Structure and Dynamics, 39(14), 5294-5304.
  12. Cichosz, P., Jarząb, J., & Tadeusiewicz, R. (2017). Computer-aided diagnostic system for nail psoriasis. Biomedical Engineering / Biomedizinische Technik, 62(1), 95-106.
  13. Azzeh, F., Al-Betar, M. A., & Almajali, A. (2018). Deep learning approach for nail diseases diagnosis. Journal of medical systems, 42(3), 48.
  14. Lee, H. S., Kim, S. J., Kang, J. M., Kang, H., & Lim, Y. M. (2019). A transfer learning approach for diagnosing nail diseases. Applied Sciences, 9(10), 2005.
  15. A.Esteva, B. Kuprel, R. A. Novoa and J. Ko, "Dermatologist-level classification of skin cancer with deep neural networks," 2017.
  16. X. Shen and J. Zhang, "An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network," 2018.
  17. H. Wen, W. Yu, Y. W. Wu, J. Zhao, X. Liu, Z. Kuang and R. Fan, "Acne detection and severity evaluation with interpretable convolutional neural network models," 2022.
  18. F. Vasefi, W. Kemp, N. MacKinnon and M. Amini, "Automated facial acne assessment from smartphone images," 2018.
  19. Kim Y. J., Park J. H., Lee B. K., Kim T. H., Lee H. Y., & Cho Y. C., "Prediction of outcomes after first-aid treatment of cardiac arrest using decision tree algorithms," 2017.
  20. Lee S. H., Kim D. H., Kim Y. H., Kang H. G., Kim S. W., & Cho S. , "Prediction of patient outcome using a random forest algorithm in cases of traumatic brain injury," 2018.
  21. Tomašev N., Glorot X., Rae J. W., Zielinski M., Askham H., Saraiva A., & Weller H., "A clinically applicable approach to continuous prediction of future acute kidney injury," 2015.