- author = "Seyed Mohammad Jafar Jalali and Abbas Khosravi and Roohallah Alizadehsani and Syed Moshfeq Salaken and Parham Mohsenzadeh Kebria and Rishi Puri and Saeid Nahavandi",
- title = "Parsimonious Evolutionary-based Model Development for Detecting Artery Disease",
- booktitle = "2019 IEEE International Conference on Industrial Technology (ICIT)",
- year = "2019",
- pages = "800--805",
- month = feb,
- keywords = "genetic algorithms, genetic programming",
-
DOI = "
doi:10.1109/ICIT.2019.8755107",
- ISSN = "2643-2978",
- abstract = "Coronary artery disease (CAD) is the most common cardiovascular condition. It often leads to a heart attack causing millions of deaths worldwide. Its accurate prediction using data mining techniques could reduce treatment risks and costs and save million lives. Motivated by these, this study proposes a framework for developing parsimonious models for CAD detection. A novel feature selection method called weight by Support Vector Machine is first applied to identify most informative features for model development. Then two evolutionary-based models called genetic programming expression (GEP) and genetic algorithm-emotional neural network (GA-ENN) are implemented for CAD prediction. Obtained results indicate that the GEP models outperform GA-ENN models and achieve the state of the art accuracy of 9percent. Such a precise model could be used as an assistive tool for medical diagnosis as well as training purposes.",
-
notes = "
Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Australia
Also known as \cite{8755107}",
