INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Medical and healthcare-related material on the Internet has expanded dramatically in recent years. On the one hand, recent research found that the number of internet users searching for health-related information online is increasing. Artificial intelligence (AI) technology IS now extensively employed to assist with knowledge acquisition and decision-making in a range of sectors. Particularly in terms of health information systems, AI has a lot to offer. The healthcare sector has recently seen a growth in the importance of research and production related to symptom-based sickness prediction. Several scientists as well as organizations have demonstrated an interest in analyzing and developing innovative techniques for rapidly and correctly predicting diseases using modern computer technologies. We provide a model for evaluating the effectiveness of merging In this research, we integrate machine learning (ML) & natural language processing (NLP) techniques into an illness prediction system. Pattern recognition in medical data is one application of machine learning. As a result, it is capable of accurately forecasting sickness. We employed Machine Learning methods, Python Programming with Jupyter Interface, as well as a dataset obtained from Kaggle to accomplish Disease Prediction based on Symptoms. The prediction power and illness categorization patterns of the NLP and ML models differed. We examined feature correlation using a confusion matrix. The proposed modes are 99% accurate. Our novel ML models obtained great illness prediction efficiency via disease categorization. This research will be valuable in illness prediction and diagnosis.
Keywords:
Machine Learning, Health Care, Chatbot, NLP, SVM, Python
Cite Article:
"Voice Interactive Bilingual Smart Healthcare Chatbot Using NLP", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.7, Issue 12, page no.c257-c268, December-2022, Available :http://www.ijnrd.org/papers/IJNRD2212230.pdf
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ISSN:
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
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