Machine Aided Diagnosis System

—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. Index Terms— AI Chatbot, Disease Prediction, Healthcare Accessibility, Machine Learning, Random Forest.

Authors

Publication Details

Journal
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Published
2025-03-30
DOI
https://doi.org/10.55041/ijsrem43375
Primary Topic
Artificial Intelligence in Healthcare
Type
article
Field-Weighted Citation Impact
0.00
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article

Machine Aided Diagnosis System

Yaswanth Mudili
INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Artificial Intelligence in Healthcare
article

Machine Aided Diagnosis System

Yaswanth Mudili
article en

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. Index Terms— AI Chatbot, Disease Prediction, Healthcare Accessibility, Machine Learning, Random Forest.

INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENTVol. 09(03)
Openalex Percentile: Top 94%
Artificial Intelligence in Healthcare
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