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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM43375

Machine Aided Diagnosis System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM43375

Volume:

9 |

3 |

Mar 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM43375

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

AV

Anupama Vanaparthi1

MA

Murali Aravelli2

YM

Yaswanth Mudili3

VKG

Vinay Kumar Gadula4

RR

Rajeswari Ravada5

VSG

Venkata Siri Gullipalli6

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Abstract

Machine-Aided Diagnosis System is an intelligent healthcare solution designed to enhance diagnostic accuracy and accessibility through machine learning and AI-driven interactions. The system employs the Random Forest algorithm to analyze multiple symptom correlations, enabling precise disease prediction based on both general and chronic symptoms. An interactive disease dashboard provides a comprehensive symptom profile, suggested specialists, and available treatment methods, ensuring users can interpret their health conditions effectively. Additionally, an LLM( Large Language Model )-powered chatbot offers real-time conversations, assisting users with symptom explanations, treatment suggestions, and specialist recommendations. By integrating machine learning for prediction, interactive visualization for clarity, and AI-driven conversations for support, the system enhances user experience and medical accessibility. This innovative approach bridges the gap between advanced healthcare technology and everyday diagnostic needs, making disease prediction more intuitive and efficient.

KEYWORDS

AI Chatbot Disease Prediction Healthcare Accessibility Machine Learning Random Forest.

Cite This Article

Anupama Vanaparthi1, Murali Aravelli2, Yaswanth Mudili3, Vinay Kumar Gadula4, Rajeswari Ravada5, Venkata Siri Gullipalli6, "Machine Aided Diagnosis System," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 3, Mar 2025. https://doi.org/10.55041/IJSREM43375

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Publication Date

3/30/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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1

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0

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5

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Conflict of Interest

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

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No funding information available for this research.

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