Abstract:
Coronary heart disease (CHD) is a disease common to both men and women and also use of
genetics have been rarely used to predict it.
Coronary heart disease alludes to a narrowing of the coronary veins, the veins that supply oxygen
and blood to the heart. It is otherwise called coronay artery disease. It is a note worthy reason for
ailment and death. The existing Clinical Decision Support Systems (CDSSs) have not been
accurate enough in their prediction and diagnosis of coronary heart disease.
By using genetic information such as Single Nucleotide Polymorphism, Genome Build,
Chromosome, Map and the partition coefficient (LogP) gotten from the Duke 2007 dataset and the
C4.5 decision tree pattern classification algorithm which was selected amongst other competing
classification algorithms including K-Nearest Neighbor, Bayes Classifier and Support Vector
Machine after a thorough evaluation on the Waikato Environment for Knowledge Analysis
(WEKA version 3.6.7), this study developed a framework for accurate prediction of coronary heart
disease. A prediction accuracy of 61.0734% was obtained from training the C4.5 algorithm on the
Duke 2007 dataset which gives higher prediction accuracy than the existing CHD Modeling and
Execution framework.
An improved framework that enhances the classification/prediction of coronary heart disease
which helps to guide patients with Cronary heart disease as to how to best manage their health
condition and live a normal life.
