A Novel Approach for Cardiac Disease Prediction and Classification Using Intelligent Agents

TL;DR

This paper presents a new intelligent agent-based system for predicting and classifying cardiac diseases by preprocessing symptoms, analyzing dependencies, and categorizing disease severity levels.

Contribution

It introduces a novel intelligent agent framework combining symptom preprocessing, dependency analysis, and probabilistic classification for cardiac disease diagnosis.

Findings

Effective symptom preprocessing with filter and wrapper agents.

Accurate classification into five severity classes.

Validated cooperative approach for cardiac disease prediction.

Abstract

The goal is to develop a novel approach for cardiac disease prediction and diagnosis using intelligent agents. Initially the symptoms are preprocessed using filter and wrapper based agents. The filter removes the missing or irrelevant symptoms. Wrapper is used to extract the data in the data set according to the threshold limits. Dependency of each symptom is identified using dependency checker agent. The classification is based on the prior and posterior probability of the symptoms with the evidence value. Finally the symptoms are classified in to five classes namely absence, starting, mild, moderate and serious. Using the cooperative approach the cardiac problem is solved and verified.