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DocBot: A System for Disease Detection and Specialized Doctor Recommendation Using Patient’s Speech of Symptoms

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Emerging Technologies in Computing (iCETiC 2023)

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Abstract

Nowadays, Machine Learning (ML) plays a crucial role in improving healthcare by enabling researchers, doctors, and patients to explore, diagnose, and prevent diseases such as dengue, typhoid, jaundice, pneumonia, and other major ailments. Our research focuses on leveraging ML to detect various diseases from a patient’s speech. The patient will describe their symptoms to the machine, akin to explaining their concerns to a doctor. The machine will then identify the disease and provide primary medication recommendations along with suggesting a specialized doctor for that particular ailment. To optimize our system’s performance, we trained our machine using multiple algorithms and evaluated their results. Our evaluation revealed an accuracy of 86.59% for Naive Bayes, 83.17% for Unhyperd SVM, 98.05% for Hyperd SVM, 97.4% for Decision Tree, and the highest accuracy of 99.35% was achieved by Random Forest.

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References

  1. 67% of all deaths in Bangladesh due to non-communicable diseases. https://www.thedailystar.net/health/disease/news/rising-health-risk-2948321. Accessed 04 June 2023

  2. What is Machine Learning in Health Care? Applications and Opportunities—Coursera. https://www.coursera.org/articles/machine-learning-in-health-care. Accessed 04 June 2023

  3. Leong, J.Y.I., Booma, P.M.: Symptom-based disease prediction system using machine learning. J. Theor. Appl. Inf. Technol. 98(19) (2020)

    Google Scholar 

  4. Fatima, M., Pasha, M., et al.: Survey of machine learning algorithms for disease diagnostic. J. Intell. Learn. Syst. Appl. 9(01), 1 (2017)

    Google Scholar 

  5. Zhao, R.-W., Li, G.-Z., Liu, J.-M., Wang, X.: Clinical multi-label free text classification by exploiting disease label relation. In: 2013 IEEE International Conference on Bioinformatics and Biomedicine, pp. 311–315 (2013)

    Google Scholar 

  6. Biswas, E., Das, A.K.: Symptom-based disease detection system in Bengali using convolution neural network. In: 2019 7th International Conference on Smart Computing Communications (ICSCC), pp. 1–5 (2019)

    Google Scholar 

  7. Hamsagayathri, P., Vigneshwaran, S.: Symptoms based disease prediction using machine learning techniques. In: 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), pp. 747–752 (2021)

    Google Scholar 

  8. Kolkur, M.S., Kalbande, D.R., Kharkar, V.: Machine learning approaches to multi-class human skin disease detection. Int. J. Comput. Intell. Res. 14(1), 29–39 (2018)

    Google Scholar 

  9. Li, J.P., Haq, A.U.L., Din, S.U., Khan, J., Khan, A., Saboor, A.: Heart disease identification method using machine learning classification in e-healthcare. IEEE Access 8, 107562–107582 (2020)

    Article  Google Scholar 

  10. Vijava Shetty, S., Karthik, G.A., Ashwin, M.: Symptom based health prediction using data mining. In: 2019 International Conference on Communication and Electronics Systems (ICCES), pp. 744–749 (2019)

    Google Scholar 

  11. Yi, Y., Shen, Z., Bompelli, A., Yu, F., Wang, Y., Zhang, R.: Natural language processing methods to extract lifestyle exposures for Alzheimer’s disease from clinical notes. In: 2020 IEEE International Conference on Healthcare Informatics (ICHI), pp. 1–2 (2020)

    Google Scholar 

  12. Hughes, M., Li, I., Kotoulas, S., Suzumura, T.: Medical text classification using convolutional neural networks. Stud. Health Technol. Inform. 235, 246–250 (2017). PMID: 28423791

    Google Scholar 

  13. Bhuiyan, M., Ullah, R., Das, A.: iHealthcare: predictive model analysis concerning big data applications for interactive healthcare systems \(\dagger \). Appl. Sci. 9, 3365 (2019). https://doi.org/10.3390/app9163365

    Article  Google Scholar 

  14. Das, A.K., Adhikary, T., Razzaque, M.A., et al.: Big media healthcare data processing in cloud: a collaborative resource management perspective. Cluster Comput. 20, 1599–1614 (2017). https://doi.org/10.1007/s10586-017-0785-8

    Article  Google Scholar 

  15. Chen, Y.: Convolutional neural network for sentence classification. Master’s thesis, University of Waterloo (2015)

    Google Scholar 

  16. Conneau, A., Schwenk, H., Barrault, L., Lecun, Y.: Very deep convolutional networks for text classification, pp. 1107–1116 (2017). https://doi.org/10.18653/v1/E17-1104

  17. Parmar, P.S. Biju, P.K., Shankar, M., Kadiresan, N.: Multiclass text classification and analytics for improving customer support response through different classifiers. In: 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Bangalore, India, pp. 538–542 (2018). https://doi.org/10.1109/ICACCI.2018.8554881

  18. Liu, S., Tao, H., Feng, S.: Text classification research based on BERT model and Bayesian network. In: 2019 Chinese Automation Congress (CAC). Hangzhou, China, pp. 5842–5846 (2019). https://doi.org/10.1109/CAC48633.2019.8996183

  19. https://www.kaggle.com/datasets/itachi9604/disease-symptom-description-dataset

  20. Toepfner, N., et al.: Detection of human disease conditions by single-cell morpho-rheological phenotyping of blood. Elife 7, e29213 (2018)

    Article  Google Scholar 

  21. Roy, K., Chaudhuri, S.S., Ghosh, S., Dutta, S.K., Chakraborty, P., Sarkar, R.: Skin disease detection based on different segmentation techniques. In: 2019 International Conference on Opto-Electronics and Applied Optics (Optronix), Kolkata, India, pp. 1–5 (2019). https://doi.org/10.1109/OPTRONIX.2019.8862403

  22. Kirar, A.T.: Machine learning based heart disease detection system. In: 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). Ankara, Turkey, pp. 1–7 (2022). https://doi.org/10.1109/HORA55278.2022.9799987

  23. Dhal, P., Azad, C.: A multi-stage multi-objective GWO based feature selection approach for multi-label text classification. In: 2022 2nd International Conference on Intelligent Technologies (CONIT), Hubli, India, pp. 1–5 (2022). https://doi.org/10.1109/CONIT55038.2022.9847886

  24. Fadil, R., Huether, A., Brunnemer, R., Blaber, A.P., Lou, J.-S., Tavakolian, K.: Early detection of parkinson’s disease using center of pressure data and machine learning. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico, pp. 2433–2436 (2021). https://doi.org/10.1109/EMBC46164.2021.9630451

  25. Mai, W., Chen, Y., Lin, X.: Early detection of neurological degenerative diseases based on the protein chirality detection with microwaves. In: 2020 IEEE Asia-Pacific Microwave Conference (APMC), Hong Kong, pp. 965–967 (2020). https://doi.org/10.1109/APMC47863.2020.9331591

  26. Dixit, S., Gaikwad, A., Vyas, V., Shindikar, M., Kamble, K.: United Neurological study of disorders: Alzheimer’s disease, Parkinson’s disease detection, anxiety detection, and stress detection using various machine learning algorithms. In: 2022 International Conference on Signal and Information Processing (IConSIP), Pune, India, pp. 1–6 (2022). https://doi.org/10.1109/ICoNSIP49665.2022.10007434

  27. Bassiouny, R., Mohamed, A., Umapathy, K., Khan, N.: An interpretable object detection-based model for the diagnosis of neonatal lung diseases using ultrasound images. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Mexico, pp. 3029–3034 (2021). https://doi.org/10.1109/EMBC46164.2021.9630169

  28. Dheer, S., Poddar, M., Pandey, A., Kalaivani, S.: Parkinson’s disease detection using acoustic features from speech recordings. In: 2023 International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics (IITCEE), Bengaluru, India, pp. 1–4 (2023). https://doi.org/10.1109/IITCEE57236.2023.10090464

  29. Sheng, T., Wu, H., Yue, Z.: An English text classification method based on TextCNN and SVM. In: 2022 3rd International Conference on Electronic Communication and Artificial Intelligence (IWECAI), Zhuhai, China, pp. 227–231 (2022). https://doi.org/10.1109/IWECAI55315.2022.00052

  30. Yu, B., Deng, C., Bu, L.: Policy text classification algorithm based on BERT. In: 2022 11th International Conference of Information and Communication Technology (ICTech), Wuhan, China, pp. 488–491 (2022). https://doi.org/10.1109/ICTech55460.2022.00103

  31. Hasan, S.A., et al.: Classification of multi-labeled text articles with reuters dataset using SVM. In: 2022 International Conference on Science and Technology (ICOSTECH), Batam City, Indonesia, pp. 01–05 (2022). https://doi.org/10.1109/ICOSTECH54296.2022.9829153

  32. Chen, S., Kuang, Q., Yu, X., Li, S., Ding, R.: A multi-label classification algorithm for non-standard text. In: 2022 International Conference on Asian Language Processing (IALP), Singapore, Singapore, pp. 206–211 (2022). https://doi.org/10.1109/IALP57159.2022.9961273

  33. Yao, T., Zhai, Z., Gao, B.: Text classification model based on fastText. In: 2020 IEEE International Conference on Artificial Intelligence and Information Systems (ICAIIS), Dalian, China, pp. 154–157 (2020). https://doi.org/10.1109/ICAIIS49377.2020.9194939

  34. Sultana, R., Palit, R.: A survey on Bengali speech-to-text recognition techniques. In: 2014 9th International Forum on Strategic Technology (IFOST), Cox’s Bazar, Bangladesh, pp. 26–29 (2014). https://doi.org/10.1109/IFOST.2014.6991064

  35. Jin, G.: Application optimization of NLP system under deep learning technology in text semantics and text classification. In: 2022 International Conference on Education, Network and Information Technology (ICENIT), Liverpool, UK, pp. 279–283 (2022). https://doi.org/10.1109/ICENIT57306.2022.00068

  36. Luo, W.: Research and implementation of text topic classification based on text CNN. In: 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA), Changchun, China, pp. 1152–1155 (2022). https://doi.org/10.1109/CVIDLICCEA56201.2022.9824532

  37. Muthu, B.A., et al.: IOT based wearable sensor for diseases prediction and symptom analysis in healthcare sector. Peer-to-peer Netw. Appl. 13, 2123–2134 (2020)

    Article  Google Scholar 

  38. Health care costs: Gallup survey finds Americans borrowed \$88 billion. https://www.usatoday.com/story/news/health/2019/04/02/health-care-costs-gallup-survey-americans-borrowed-88-billion/3333864002/. Accessed 04 June 2023

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Authors and Affiliations

  1. United International University, United City, Madani Avenue, Badda, Dhaka, 1212, Bangladesh

    Jubayer Hossen, Md. Rishad Islam, Abir Chowdhury, Israt Jahan Ukti & Md. Motaharul Islam

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  1. Jubayer Hossen
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  2. Md. Rishad Islam
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  3. Abir Chowdhury
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  4. Israt Jahan Ukti
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  5. Md. Motaharul Islam
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Correspondence to Md. Motaharul Islam .

Editor information

Editors and Affiliations

  1. Xiamen University Malaysia, Selangor, Malaysia

    Mahdi H. Miraz

  2. University of Chester, Chester, UK

    Garfield Southall

  3. Universiteti Metropolitan Tirana, Tirana, Albania

    Maaruf Ali

  4. University of South Wales, Pontypridd, UK

    Andrew Ware

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Hossen, J., Islam, M.R., Chowdhury, A., Ukti, I.J., Islam, M.M. (2024). DocBot: A System for Disease Detection and Specialized Doctor Recommendation Using Patient’s Speech of Symptoms. In: Miraz, M.H., Southall, G., Ali, M., Ware, A. (eds) Emerging Technologies in Computing. iCETiC 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 538. Springer, Cham. https://doi.org/10.1007/978-3-031-50215-6_6

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  • DOI: https://doi.org/10.1007/978-3-031-50215-6_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50214-9

  • Online ISBN: 978-3-031-50215-6

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