Prediction of Coronary Heart Disease Using Discriminant Analysis Algorithm in Active Elderly Men

Coronary heart disease (CHD) is an important medical disorder and one of the most common heart diseases worldwide, which causes disability and economic burden. The medical and research community is increasingly interested in computer-aided coronary heart disease diagnosis through the use of machine learning methods. This study aimed to diagnose coronary heart disease using a discriminant analysis algorithm in active elderly men.

This analytical study was conducted on 351 patients of Ayatollah Kashani Hospital in Tehran. This work used discriminant analysis algorithm to diagnose coronary artery disease. Python software was used for data analysis.

The results showed that by using 14 characteristics as risk factors related to the subjects' laboratory, personal and lifestyle information. The discriminant analysis algorithm could distinguish healthy and sick people with 94.4% accuracy and 88.9% precision.

The results of the present study showed that this system can probably be used as an effective and intelligent method along with other diagnostic methods by cardiologists to predict coronary artery disease. Also, new data mining methods can be effective in reducing invasive risks.