Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{156910,
        author = {Avina Wakchaure and Shamli Kavle and Shruti Surve and Apurva Patil},
        title = {MEDBOT: A MEDICAL CHATBOT FOR DISEASE DETECTION AND SUGGESTION THROUGH MACHINE LEARNING MODELS},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {5},
        pages = {397-403},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=156910},
        abstract = {The Healthcare sector is one of the fastest growing and highly competitive sector that has been improving with the insertion of technology into this paradigm. The healthcare sector has been facing a severe lack of medical professionals that has been evident in the recent pandemic. The recent pandemic exposed the dearth of doctors and other staff in the hospitals and clinics. This is a problematic issue that will require a lot of effective improvements but the lack of the professionals has been an issue that has lasting consequences right away. The doctors that are consulting are overburdened with the patients and the elderly individuals have a hard time travelling to the hospital every time for a consultation. Therefore, the paradigm of remote diagnosis comes to the rescue. For this purpose, an effective approach called MedBot is proposed in this research article that utilizes K Nearest Neighbor clustering and Linear Regression along with Artificial Neural Networks and Decision Making. The approach has been effectively evaluated for its disease prediction and has resulted in extremely satisfactory results.},
        keywords = {K nearest Neighbors, Linear Regression, Artificial Neural Networks and Decision Tree},
        month = {},
        }