Web-Based Decision Support System for Coronary Heart Disease Diagnosis

Area of Science:

• Cardiology
• Medical Informatics
• Artificial Intelligence

Background:

• Coronary heart disease (CHD) is a prevalent and severe condition impacting public health.
• Accurate diagnosis is crucial for effective treatment and patient health management.
• Machine learning (ML) offers potential for precise and timely disease diagnosis.

Purpose of the Study:

• To evaluate the efficacy of popular machine learning algorithms in diagnosing coronary heart disease.
• To develop a user-friendly, web-based decision support system for CHD diagnosis.

Main Methods:

• Application of various machine learning algorithms to a dataset for CHD diagnosis.
• Development of a web-based decision support tool utilizing easily accessible patient data.

Main Results:

• Achieved diagnostic performance exceeding 70% with the applied ML algorithms.
• Performance levels were comparable to, and in some instances surpassed, existing literature.
• The developed system provides a practical tool for medical professionals.

Conclusions:

• Machine learning models demonstrate significant potential for accurate coronary heart disease diagnosis.
• The developed decision support system can aid healthcare professionals in clinical practice.
• This approach facilitates earlier and more reliable identification of coronary heart disease.