Parsimonious Evolutionary-based Model Development for Detecting Artery Disease

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 90%. Such a precise model could be used as an assistive tool for medical diagnosis as well as training purposes.

[1]  Cândida Ferreira.  Gene Expression Programming in Problem Solving , 2002 .

[2]  Han Woo Park,et al.  Conversations about Open Data on Twitter , 2017 .

[3]  Jafar Habibi,et al.  A data mining approach for diagnosis of coronary artery disease , 2013, Comput. Methods Programs Biomed..

[4]  E. Braunwald.  Heart Disease: A Textbook of Cardiovascular Medicine , 1992, Annals of Internal Medicine.

[5]  Sérgio Moro,et al.  Analytical assessment process of e-learning domain research between 1980 and 2014 , 2018 .

[6]  Saeid Nahavandi,et al.  Wind power forecasting using emotional neural networks , 2014, 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[7]  Ehsan Lotfi,et al.  A winner-take-all approach to emotional neural networks with universal approximation property , 2015, Inf. Sci..

[8]  Roohollah Shirani Faradonbeh,et al.  Long-term prediction of rockburst hazard in deep underground openings using three robust data mining techniques , 2018, Engineering with Computers.

[9]  Saeid Nahavandi,et al.  Hierarchical Gene Selection and Genetic Fuzzy System for Cancer Microarray Data Classification , 2015, PloS one.

[10]  Michele Marchesi,et al.  A hybrid genetic-neural architecture for stock indexes forecasting , 2005, Inf. Sci..

[11]  Paulo Cortez,et al.  Using customer lifetime value and neural networks to improve the prediction of bank deposit subscription in telemarketing campaigns , 2014, Neural Computing and Applications.

[12]  Iman Raeesi Vanani,et al.  A comparative analysis of emerging scientific themes in business analytics , 2018, Int. J. Bus. Inf. Syst..

[13]  Sérgio Moro,et al.  Can we trace back hotel online reviews' characteristics using gamification features? , 2019, Int. J. Inf. Manag..

[14]  Sérgio Moro,et al.  A comparative analysis of classifiers in cancer prediction using multiple data mining techniques , 2017 .

[15]  Paulo Cortez,et al.  A data-driven approach to predict the success of bank telemarketing , 2014, Decis. Support Syst..

[16]  Giuliano Armano,et al.  RANKS: a flexible tool for node label ranking and classification in biological networks , 2016, Bioinform..

[17]  Masoud Monjezi,et al.  Genetic programming and gene expression programming for flyrock assessment due to mine blasting , 2016 .

[18]  Chee Peng Lim,et al.  Medical image analysis using wavelet transform and deep belief networks , 2017, Expert Syst. Appl..

[19]  Saeid Nahavandi,et al.  A sequential search-space shrinking using CNN transfer learning and a Radon projection pool for medical image retrieval , 2018, Expert Syst. Appl..

[20]  Seyed Mohammad Jafar Jalali,et al.  A data mining framework for classification of organisational performance based on rough set theory , 2018 .

[21]  Manoj Khandelwal,et al.  A new model based on gene expression programming to estimate air flow in a single rock joint , 2016, Environmental Earth Sciences.

[22]  Sayed Farhad Mousavi,et al.  Modeling of Fixed-Bed Column System of Hg(II) Ions on Ostrich Bone Ash/nZVI Composite by Artificial Neural Network , 2017 .

[23]  Roohallah Alizadehsani,et al.  Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm , 2017, Comput. Methods Programs Biomed..

[24]  Giuliano Armano,et al.  Early diagnosis of heart disease using classification and regression trees , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[25]  Han Woo Park,et al.  State of the art in business analytics: themes and collaborations , 2018 .

[26]  Jafar Habibi,et al.  Coronary artery disease detection using computational intelligence methods , 2016, Knowl. Based Syst..

[27]  Iman Raeesi Vanani,et al.  Analytical evaluation of emerging scientific trends in business intelligence through the utilisation of burst detection algorithm , 2017 .

[28]  Paulo Rita,et al.  Stripping customers' feedback on hotels through data mining: the case of Las Vegas Strip , 2017 .

[29]  Seyed Mohammad JafarJalali.  Visualizing e-government emerging and fading themes using SNA techniques , 2016, 2016 10th International Conference on e-Commerce in Developing Countries: with focus on e-Tourism (ECDC).

[30]  Saeid Nahavandi,et al.  A deep-structural medical image classification for a Radon-based image retrieval , 2017, 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE).

[31]  Saeid Nahavandi,et al.  Non-invasive detection of coronary artery disease in high-risk patients based on the stenosis prediction of separate coronary arteries , 2018, Comput. Methods Programs Biomed..

[32]  Paulo Cortez,et al.  Using data mining for bank direct marketing: an application of the CRISP-DM methodology , 2011 .

[33]  Saeid Nahavandi,et al.  Mass spectrometry cancer data classification using wavelets and genetic algorithm , 2015, FEBS letters.