JCEEES

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

Index
Original Article
Prediction of heart attack risk using linear discriminant analysis methods
Aims: The interruption of blood flow to the heart muscle is called a heart attack. During a heart attack, the risk of permanent damage increases with every second the heart tissue cannot receive enough blood. If early and appropriate intervention is not performed, loss of heart tissue occurs. Causes such as smoking, cholesterol, diabetes, high blood pressure, old age, obesity, genetics, and high levels of certain substances produced in the liver are the main risk factors for heart attack. This study aims to predict the risk of heart attack with machine learning methods using a dataset created by considering risk factors.
Methods: The performances of three types of Linear Discriminant Analysis classifiers, Normal, Ledoit-Wolf, and Oracle Shrinkage Approximating, were compared on the Cleveland dataset.
Results: Normal Linear Discriminant Analysis made the best classification with 83.60% accuracy and performed better than regularized versions.
Conclusion: Linear Discriminant Analysis methods are a promising classifier for heart attack prediction and can be applied in hospitals as an objective and automated system that eases specialists' workload and helps reduce diagnostic costs.


1. Aec, I., Akhil Jabbar, M., Deekshatulu, B. L., Priti, Akhil Jabbar, C. M.,& Chandra, P. (2013). Classification of Heart Disease using ArtificialNeural Network and Feature Subset Selection. Global Journal ofComputer Science and Technology, 13(D3), 5-14.
2. Ajam, N. (2015). Heart Diseases Diagnoses using Artificial NeuralNetwork. Network and Complex Systems , 5(4).
3. Asl, B. M., Setarehdan, S. K., & Mohebbi, M. (2008). Support vectormachine-based arrhythmia classification using reduced features ofheart rate variability signal. Artificial Intelligence in Medicine, 44(1),51-64. https://doi.org/10.1016/J.ARTMED.2008.04.007
4. Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., &Singh, P. (2021). Prediction of Heart Disease Using a Combination ofMachine Learning and Deep Learning. Computational Intelligence andNeuroscience, 2021. https://doi.org/10.1155/2021/8387680
5. Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O. (2010). Shrinkagealgorithms for MMSE covariance estimation. IEEE Transactionson Signal Processing, 58(10), 5016-5029. https://doi.org/10.1109/TSP.2010.2053029
6. Das, R., Turkoglu, I., & Sengur, A. (2009). Effective diagnosis ofheart disease through neural networks ensembles. Expert Systemswith Applications, 36(4), 7675-7680. https://doi.org/10.1016/J.ESWA.2008.09.013
7. Detrano, R. (1989). Cleveland heart disease database. VA MedicalCenter, Long Beach and Cleveland Clinic Foundation.
8. Dissanayake, K., & Johar, M. G. M. (2021). Comparative study on heartdisease prediction using feature selection techniques on classificationalgorithms. Applied Computational Intelligence and Soft Computing,2021. https://doi.org/10.1155/2021/5581806
9. Dudoit, S., Fridlyand, J., & Speed, T. P. (2011). Comparison ofDiscrimination Methods for the Classification of Tumors Using GeneExpression Data.Journal of the American Statistical Association,2011.97(457), 77-86. https://doi.org/10.1198/016214502753479248
10. Gárate-Escamila, A. K., Hajjam El Hassani, A., & Andrès, E. (2020).Classification models for heart disease prediction using featureselection and PCA. Informatics in Medicine Unlocked, 19, 100330.https://doi.org/10.1016/J.IMU.2020.100330
11. Guidi, G., Pettenati, M. C., Melillo, P., & Iadanza, E. (2014). A machinelearning system to improve heart failure patient assistance. IEEEJournal of Biomedical and Health Informatics, 18(6), 1750-1756.https://doi.org/10.1109/JBHI.2014.2337752
12. Huang, D., Quan, Y., He, M., & Zhou, B. (2009). Comparison oflinear discriminant analysis methods for the classification of cancerbased on gene expression data. Journal of Experimental and ClinicalCancer Research, 28(1), 1-8. https://doi.org/10.1186/1756-9966-28-149/FIGURES/2
13. Izenman, A. J. (2013). Linear Discriminant Analysis. In ModernMultivariate Statistical Techniques 237-280. Springer, New York, NY.https://doi.org/10.1007/978-0-387-78189-1_8
14. Katarya, R., & Meena, S. K. (2021). Machine Learning Techniques forHeart Disease Prediction: A Comparative Study and Analysis. Healthand Technology, 11(1), 87-97. https://doi.org/10.1007/S12553-020-00505-7/TABLES/3
15. Latha, C. B. C., & Jeeva, S. C. (2019). Improving the accuracy ofprediction of heart disease risk based on ensemble classificationtechniques. Informatics in Medicine Unlocked, 16, 100203. https://doi.org/10.1016/J.IMU.2019.100203
16. Ledoit, O., & Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis,88(2), 365-411. https://doi.org/10.1016/S0047-259X(03)00096-4
17. Li, H., Zhang, K., & Jiang, T. (2005). Robust and accurate cancerclassification with gene expression profiling. Proceedings. IEEEComputational Systems Bioinformatics Conference, 2005, 310-321.https://doi.org/10.1109/CSB.2005.49
18. Manimurugan, S., Almutairi, S., Aborokbah, M. M., Narmatha,C., Ganesan, S., Chilamkurti, N., Alzaheb, R. A., & Almoamari, H.(2022). Two-Stage Classification Model for the Prediction of HeartDisease Using IoMT and Artificial Intelligence. Sensors 2022,22(2).https://doi.org/10.3390/S22020476
19. Negi, S., Kumar, Y., & Mishra, V. M. (2016). Feature extraction andclassification for EMG signals using linear discriminant analysis.Proceedings - 2016 International Conference on Advances inComputing, Communication and Automation (Fall), ICACCA 2016.https://doi.org/10.1109/ICACCAF.2016.7748960
20. Santhanam, T., & Ephzibah, E. P. (2013). Heart disease classificationusing PCA and feed forward neural networks. Lecture Notes inComputer Science (Including Subseries Lecture Notes in ArtificialIntelligence and Lecture Notes in Bioinformatics), 8284 LNAI, 90-99. https://doi.org/10.1007/978-3-319-03844-5_10/COVER
21. Sharma, A., & Paliwal, K. K. (2008). Cancer classification by gradientLDA technique using microarray gene expression data. Data &Knowledge Engineering, 66(2), 338-347. https://doi.org/10.1016/J.DATAK.2008.04.004
22. Singh, R. S., Saini, B. S., & Sunkaria, R. K. (2018). Detection ofcoronary artery disease by reduced features and extreme learningmachine. Clujul Medical, 91(2), 166. https://doi.org/10.15386/CJMED-882
23. Srinivas, K., Raghavendra Rao, G., & Govardhan, A. (2010). Analysisof coronary heart disease and prediction of heart attack in coalmining regions using data mining techniques. ICCSE 2010 - 5thInternational Conference on Computer Science and Education, FinalProgram and Book of Abstracts, 1344-1349. https://doi.org/10.1109/ICCSE.2010.5593711
24. Thenmozhi, K., & Deepika, P. (2014). Heart Disease PredictionUsing Classification with Different Decision Tree Techniques.International Journal of Engineering Research and General Science,2(6), 6-11.
25. Virani, S. S., Alonso, A., Aparicio, H. J., Benjamin, E. J., Bittencourt,M. S., Callaway, C. W., Carson, A. P., Chamberlain, A. M., Cheng,S., Delling, F. N., Elkind, M. S. V., Evenson, K. R., Ferguson, J.F., Gupta, D. K., Khan, S. S., Kissela, B. M., Knutson, K. L., Lee,C. D., Lewis, T. T., Tsao, C. W. (2021). Heart Disease and StrokeStatistics - 2021 Update: A Report From the American HeartAssociation. Circulation, 143(8), E254-E743. https://doi.org/10.1161/CIR.0000000000000950/FORMAT/EPUB
26. Wettschereck, D., & Dietterich, T. G. (1995). An experimentalcomparison of the nearest-neighbor and nearest-hyperrectanglealgorithms. Machine Learning, 19(1), 5-27. https://doi.org/10.1007/BF00994658
27. Xing, Y., Wang, J., Zhao, Z., & Gao, andYonghong. (2008).Combination Data Mining Methods with New Medical Data toPredicting Outcome of Coronary Heart Disease. 868-872. https://doi.org/10.1109/ICCIT.2007.204
Volume 1, Issue 1, 2023
Page : 5-9
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