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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM56493

Medbot: A Deep Learning-Based Medical Chatbot for Symptom Detection and Suggestion

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM56493

Volume:

10 |

2 |

Feb 2026

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

VST1

V. S. Tamboli 1

BYN

Bhalerao Yash Nilesh2

TOA

Tupe Omkar Ashok3

DGR

Dighe Gaurav Ramnath3

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Abstract

To live a long and healthy life, which is attainable for many people on this planet, the healthcare industry is crucial. These people make use of medical facilities for treatment, routine examinations, and symptom-based disease diagnosis. By analyzing symptoms, these medical care facilities accurately diagnose a variety of ailments and provide appropriate therapy. But these facilities are under a lot of pressure, and doctors are overworked, because of the huge increase in the global population and the number of people. Hence, technology needs to be integrated into this method to make it more convenient for both patients and health care professionals. For this reason, a Chabot can serve patients well by automatically making accurate diagnoses. Thus, a highly efficient method for an automated Chabot that makes use of Decision Trees, Deep Belief Networks, Linear Regression, and K Nearest Neighbors Clustering. This strategy has been thoroughly tested using the methods described in this paper.

KEYWORDS

K- nearest Neighbor classification Regression analysis Hidden Markov model Decision Tree.

Cite This Article

V. S. Tamboli 1, Bhalerao Yash Nilesh2, Tupe Omkar Ashok3, Dighe Gaurav Ramnath3, "Medbot: A Deep Learning-Based Medical Chatbot for Symptom Detection and Suggestion," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 2, Feb 2026.

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Publication Date

11/1/2026

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Double-Blind Peer Review

Review Rounds: 2

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0

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3

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5

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The authors declare no conflict of interest related to this research work.

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