Implementation of Software Agents and Advanced AoA for Disease Data Analysis

Area of Science:

• Artificial Intelligence
• Medical Informatics
• Computational Linguistics

Background:

• Traditional diagnostic approaches can yield contradictory solutions.
• Manual analysis of extensive medical data is time-consuming and prone to errors.

Purpose of the Study:

• To propose an Intelligent Searching Approach (ISA) that replaces traditional Agent oriented Approach (AoA).
• To enhance diagnostic accuracy and efficiency by utilizing intelligent artificial agents.

Main Methods:

• Employing intelligent artificial agents to dynamically analyze medical forums and blogs.
• Utilizing Agent Communication Language (ACL) and FIPA for inter-agent communication.
• Implementing a Global Agent to control local agents, consolidate solutions, and make final decisions.
• Applying advanced clustering techniques for a prioritization matrix of suggested solutions.
• Employing a recursive refining process to ensure optimal solution selection.

Main Results:

• Achieved more accurate findings compared to manual approaches.
• Minimized communication time between agents through centralized control by the Global Agent.
• Generated a prioritized list of best solutions via clustering.
• Successfully refined solutions recursively to ensure perfection.

Conclusions:

• The Intelligent Searching Approach (ISA) offers a novel and efficient method for obtaining accurate diagnostic solutions.
• This AI-driven approach significantly reduces time spent on data research and inter-agent communication.
• The system ensures solution perfection without compromising efficiency.