Academic Journal
Outdoor patient classification in hospitals based on symptoms in Bengali language
| العنوان: | Outdoor patient classification in hospitals based on symptoms in Bengali language |
|---|---|
| المؤلفون: | Jannatul Ferdous Ruma, Fayezah Anjum, Rokeya Siddiqua, Md. Abidur Rahman, Abir Hossain Rohan, Rashedur M. Rahman |
| المصدر: | Journal of Information and Telecommunication, Vol 7, Iss 3, Pp 336-358 (2023) |
| بيانات النشر: | Taylor & Francis Group, 2023. |
| سنة النشر: | 2023 |
| المجموعة: | LCC:Telecommunication LCC:Information technology |
| مصطلحات موضوعية: | Healthcare, NLP, Bengali text classification, machine learning, deep learning, Telecommunication, TK5101-6720, Information technology, T58.5-58.64 |
| الوصف: | ABSTRACTIn recent years, Bangladesh has seen significant development in the digitalization of various healthcare services. Although many mobile applications and social platforms have been developed to automate the services of the healthcare sector, there is still scope to make the process smooth and easily accessible for general people. This paper describes a system where the users can give their health-related problems or symptoms in the native Bengali language, and the system would recommend the medical specialist the user should visit based on their stated symptoms. The data is processed using various Natural Language Processing techniques. In this study, we have applied both Machine Learning and Deep Learning-based approaches. Three different models of Machine learning and four models of deep learning have been applied, analyzed and the accuracy of various models is evaluated to determine the best one that could provide superior performance on the given dataset. From the pool of traditional machine learning algorithms, the Random Forest (RF) classifier gives the highest accuracy of about 94.60% and Convolutional Neural Network performs the best among the deep-learning models, with an accuracy of 94.17%. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 24751839 2475-1847 2475-1839 |
| Relation: | https://doaj.org/toc/2475-1839; https://doaj.org/toc/2475-1847 |
| DOI: | 10.1080/24751839.2023.2196106 |
| URL الوصول: | https://doaj.org/article/0568aaba013a4b028156e86a728956e1 |
| رقم الانضمام: | edsdoj.0568aaba013a4b028156e86a728956e1 |
| قاعدة البيانات: | Directory of Open Access Journals |
| تدمد: | 24751839 24751847 |
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| DOI: | 10.1080/24751839.2023.2196106 |