CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

A System for Diagnosis of Coronary Artery Disease based on Neural Networks and Machine Learning Algorithms

by Najmeh Samadiani, Zeinab Hassani
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 15
Year of Publication: 2018
Authors: Najmeh Samadiani, Zeinab Hassani
10.5120/ijca2018917784

Najmeh Samadiani, Zeinab Hassani . A System for Diagnosis of Coronary Artery Disease based on Neural Networks and Machine Learning Algorithms. International Journal of Computer Applications. 181, 15 ( Sep 2018), 36-41. DOI=10.5120/ijca2018917784

@article{ 10.5120/ijca2018917784,
author = { Najmeh Samadiani, Zeinab Hassani },
title = { A System for Diagnosis of Coronary Artery Disease based on Neural Networks and Machine Learning Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 181 },
number = { 15 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 36-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number15/29900-2018917784/ },
doi = { 10.5120/ijca2018917784 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:06:05.576118+05:30
%A Najmeh Samadiani
%A Zeinab Hassani
%T A System for Diagnosis of Coronary Artery Disease based on Neural Networks and Machine Learning Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 15
%P 36-41
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Today, computer aided systems play an important role in various fields of medical science such as diagnosis and treatment of diseases; therefore, selected tools should minimize error and maximize the confidence. In this study, considering the importance of cardiovascular disease in the world, the coronary artery disease is diagnosed by neural networks and machine learning algorithms. The proposed system employs three types of artificial neural networks, decision tree and Adaboost algorithm to distinguish people who suffer from heart disease and the healthy individuals using Cleveland's dataset. Among these algorithms, the multilayer perceptron neural network has the best performance and is able to predict coronary artery disease with the accuracy, sensitivity and specificity of 94.53%, 86.77%, and 99.39%, respectively. The superiority of the proposed system is obvious comparing to other existing studies because it diagnoses the disease with higher accuracy, sensitivity and more reliability.

References
  1. [Internet]. 2017 [cited 21 December 2017]. Available from: https://www.nlm.nih.gov/medlineplus/coronaryarterydisease.htm
  2. Wong N. 2014. Epidemiological studies of CHD and the evolution of preventive cardiology. Nature Reviews Cardiology. 11(5): 276-289.
  3. Buchan K, Filannino M, Uzuner Ö. 2017. Automatic prediction of coronary artery disease from clinical narratives. Journal of Biomedical Informatics. 72:23-32.
  4. Davari Dolatabadi A, Khadem S, Asl B. 2017. Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Computer Methods and Programs in Biomedicine. 138:117-126.
  5. Zengin E, Bickel C, Schnabel R, Zeller T, Lackner K, Rupprecht H et al. 2015. Risk Factors of Coronary Artery Disease in Secondary Prevention—Results from the AtheroGene—Study. PLOS ONE. 10(7):e0131434.
  6. Shouman M, Turner T, Stocker R. 2011. Using Decision Tree for Diagnosing Heart Disease Patients. AusDM '11 Proceedings of the Ninth Australasian Data Mining Conference.121, 23-30.
  7. Kochurani O.G, Aji S, Kaimal M.R. 2007. A Neuro Fuzzy Decision Tree Model for Predicting the Risk in Coronary Artery Disease. IEEE 22nd International Symposium onIntelligent Control (ISIC).
  8. El-Bialy R, Salamay M, Karam O, Khalifa M. 2015. Feature Analysis of Coronary Artery Heart Disease Data Sets. Procedia Computer Science. 65:459-468.
  9. Atkov O, Gorokhova S, Sboev A, Generozov E, Muraseyeva E, Moroshkina S et al. 2012. Coronary heart disease diagnosis by artificial neural networks including genetic polymorphisms and clinical parameters. Journal of Cardiology. 59(2):190-194.
  10. H.S N. 2013. ANN Model to Predict Coronary Heart Disease Based on Risk Factors. Bonfring International Journal of Man Machine Interface. 3(2):13-18.
  11. Das R, Turkoglu I, Sengur A. 2009. Effective diagnosis of heart disease through neural networks ensembles. Expert Systems with Applications. 36(4):7675-7680.
  12. Kurt I, Ture M, Kurum A. 2008. Comparing performances of logistic regression, classification and regression tree, and neural networks for predicting coronary artery disease. Expert Systems with Applications. 34(1):366-374.
  13. Schapire R.E. 2003. The boosting approach to machine learning: An overview. Nonlinear Estimation and Classification. Springer. 149-171.
  14. Arabasadi Z, Alizadehsani R, Roshanzamir M, Moosaei H, Yarifard A. 2017. Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Computer Methods and Programs in Biomedicine. 141:19-26.
  15. Akila S, Chandramathi S. 2015. A Hybrid Method for Coronary Heart Disease Risk Prediction using Decision Tree and Multi-Layer Perceptron. Indian Journal of Science and Technology. 8(34).
  16. Sunitha S. 2010. Data Mining of Medical Datasets with Missing Attributes from Different Sources [Master of Science in Mathematics]. Youngstown State University, Department of Mathematics and Statistics.
  17. Polat K, Şahan S, Güneş S. 2007. Automatic detection of heart disease using an artificial immune recognition system (AIRS) with fuzzy resource allocation mechanism and k-nn (nearest neighbour) based weighting preprocessing. Expert Systems with Applications. 32(2):625-631.
  18. Babaoğlu I, Fındık O, Bayrak M. 2010. Effects of principle component analysis on assessment of coronary artery diseases using support vector machine. Expert Systems with Applications. 37(3):2182-2185.
  19. Newman, D. J., Hettich, S., Blake, C. L. S., & Merz, C. J. 1998. UCI repository of machine learning database. Irvine, CA: University of California.
  20. Offor U, Alabi S. 2016. Artificial Neural Network Model for Friction Factor Prediction. Journal of Materials Science and Chemical Engineering. 04(07):77-83.
  21. Ebrahimabadi A, Azimipour M, Bahreini A. 2015. Prediction of roadheaders' performance using artificial neural network approaches (MLP and KOSFM). Journal of Rock Mechanics and Geotechnical Engineering. 7(5):573-583.
  22. Durão R, Mendes M, João Pereira M. 2016. Forecasting O 3 levels in industrial area surroundings up to 24 h in advance, combining classification trees and MLP models. Atmospheric Pollution Research. 7(6):961-970.
  23. Thoma M. 2017. Analysis and optimization of convolutional neural network architectures, master Thesis.
  24. Sa'di S, Hashemi R, Abdollapour A, Chalabi K, Salamat M. 2015. A Novel Probabilistic Artificial Neural Networks Approach for Diagnosing Heart Disease. International Journal in Foundations of Computer Science & Technology. 5(6):47-53.
  25. Specht. 1988. Probabilistic neural networks for classification, mapping, or associative memory. IEEE International Conference on Neural Networks.
  26. Ghaderzadeh M. 2014. An Intelligent System Based on Back Propagation Neural Network and Particle Swarm Optimization for Detection of Prostate Cancer from Benign Hyperplasia of Prostate. Journal of Health & Medical Informatics. 05(03).
  27. Mingoti S, Lima J. 2006. Comparing SOM neural network with Fuzzy c-means, K-means and traditional hierarchical clustering algorithms. European Journal of Operational Research. 174(3):1742-1759.
  28. Rokach L, Maimon O. 2010. Data mining with decision trees: theory and applications: Series in machine perception and artificial intelligence. World Scientific.
  29. Weihong W. 2006. A preliminary study on constructing decision tree with gene expression programming. In Proc. 1st international conference on innovative computing, information and control; 222-225.
  30. Freund Y, Schapire R. 1997. A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences. 55(1):119-139.
  31. Schapire R.E. 2003. The boosting approach to machine learning: An overview. Nonlinear Estimation and Classification. Springer; 149-171.
  32. Rodger J. 2015. Discovery of medical Big Data analytics: Improving the prediction of traumatic brain injury survival rates by data mining Patient Informatics Processing Software Hybrid Hadoop Hive. Informatics in Medicine Unlocked. 1:17-26.
  33. Sun Y, Clark O. 2009. Implementing an Intuitive Reasoner for Predicting Continuous Weather Variables. 2009 International Conference on Computer Modeling and Simulation.
Index Terms

Computer Science
Information Sciences

Keywords

Multi-layer Perceptron Neural Network (MLP) Self-Organizing Map Neural Network (SOM) Decision Tree Adaboost Coronary Artery Disease Heart Disease.

Powered by PhDFocusTM