The possible role of machine learning in detection of increased cardiovascular risk patients - KSC MR Study (design)

Area of Science:

• Cardiology
• Medical Informatics
• Machine Learning

Background:

• Current cardiovascular (CV) risk quantification methods rely on limited parameters, often underestimating actual patient risk.
• This leads to a significant number of high-risk individuals being misidentified, failing to reflect true CV risk.
• There is a critical need for more comprehensive and accurate CV risk assessment tools.

Purpose of the Study:

• To develop advanced algorithms for accurate coronary artery disease (CAD) risk prediction.
• To integrate a wide range of patient data for a more personalized medicine approach.
• To improve the identification of high-risk individuals for timely intervention.

Main Methods:

• The Kosice Selective Coronarography Multiple Risk (KSC MR) Study will analyze coronary angiography data.
• Patient characteristics will be collected through questionnaires, physical exams, and laboratory tests.
• Machine learning protocols will be applied to develop predictive algorithms.

Main Results:

• The study aims to develop algorithms incorporating all available parameters for precise CAD probability calculation.
• These algorithms are expected to significantly improve the accuracy of identifying individuals at high risk for CAD.
• The findings will enable a more realistic assessment of cardiovascular risk.

Conclusions:

• Successful implementation of the KSC MR study could establish a foundation for specialized software.
• This software would aid in identifying high-risk patients and those with potential coronary angiography findings.
• The approach aligns with the principles of personalized medicine for enhanced cardiovascular care.