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.

Original Article
Classification of heart disease dataset with k-NN optimized by pso and gwo algorithms
Lifestyle changes worldwide are increasing chronic diseases (CD). This research concluded that cardiovascular diseases (CVDs) cause 46% of global mortality, excluding communicable diseases and accidents, and heart attacks are 7.4 million of the 17.5 million CVD deaths. 2030 will see 22.2 million cardiovascular deaths. Avoiding and treating most HD reduces cardiovascular disease fatalities. In this study, the data set for heart disease is optimized with PSO and GWO, and classification is performed with k-NN.

1. Al-Tashi, Q., Rais, H., & Jadid, S. (2019). Feature selection method basedon grey wolf optimization for coronary artery disease classification.Advances in Intelligent Systems and Computing, 843, 257-266. https://doi.org/10.1007/978-3-319-99007-1_25
2. Dubey, A. K., Sinhal, A. K., & Sharma, R. (2022). An ImprovedAuto Categorical PSO with ML for Heart Disease Prediction.Engineering, Technology & Applied Science Research, 12(3), 8567-8573. https://doi.org/10.48084/etasr.4854
3. Dulhare, U. N. (2018). Prediction system for heart disease using NaiveBayes and particle swarm optimization. Biomedical Research, 29(12),2646-2649. https://doi.org/10.4066/biomedicalresearch.29-18-620
4. Hasanova, H., Tufail, M., Baek, U. J., Park, J. T., & Kim, M. S. (2022).A novel blockchain-enabled heart disease prediction mechanism usingmachine learning. Computers and Electrical Engineering, 101(May),108086. https://doi.org/10.1016/j.compeleceng.2022.108086
5. Jabbar, M. A., Deekshatulu, B. L., & Chandra, P. (2013). Classificationof Heart Disease Using K- Nearest Neighbor and Genetic Algorithm.Procedia Technology, 10, 85-94. https://doi.org/10.1016/j.protcy.2013.12.340
6. Karabulut, B., Arslan, G., & Ünver, H. M. (2019). A Weighted SimilarityMeasure for k-Nearest Neighbors Algorithm. Celal Bayar ÜniversitesiFen Bilimleri Dergisi, 15(4), 393-400. https://doi.org/10.18466/cbayarfbe.618964
7. Khourdifi, Y., & Bahaj, M. (2019). Heart disease prediction andclassification using machine learning algorithms optimizedby particle swarm optimization and ant colony optimization.International Journal of Intelligent Engineering and Systems, 12(1),242-252. https://doi.org/10.22266/ijies2019.0228.24
8. Khourdifi, Y., & Bahaj, M. (2019). The Hybrid Machine Learning ModelBased on Random Forest Optimized by PSO and ACO for PredictingHeart Disease. https://doi.org/10.4108/eai.24-4-2019.2284088
9. Li, G., Tan, Z., Xu, W., Xu, F., Wang, L., Chen, J., & Wu, K. (2021). A particleswarm optimization improved BP neural network intelligent model forelectrocardiogram classification. BMC Medical Informatics and DecisionMaking, 21(Suppl 2), 1-15. https://doi.org/10.1186/s12911-021-01453-6
10. Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey Wolf Optimizer. Advances inEngineering Software, 69, 46-61. https://doi.org/10.1016/j.advengsoft.2013.12.007
11. Muthukaruppan, S., & Er, M. J. (2012). A hybrid particle swarmoptimization based fuzzy expert system for the diagnosis of coronaryartery disease. Expert Systems with Applications, 39(14), 11657-11665.https://doi.org/10.1016/j.eswa.2012.04.036
12. Naga, M., & Asst, S. (2023). Detection of Cardiovascular Diseaseusing Machine Learning, Genetic Algorithms and Particle SwarmOptimization. IJERT, 12(03), 120-127.
13. Prasath, V. B. S., Alfeilat, H. A. A., Hassanat, A. B. A., Lasassmeh, O.,Tarawneh, A. S., Alhasanat, M. B., & Salman, H. S. E. (2017). Distance andSimilarity Measures Effect on the Performance of K-Nearest NeighborClassifier -- A Review. 1-39. https://doi.org/10.1089/big.2018.0175
14. Qois Syafi, M. (2022). Increasing Accuracy of Heart DiseaseClassification on C4.5 Algorithm based on information gain ratioand particle swarm optimization using adaboost ensemble. Journalof Advances in Information Systems and Technology, 4(1), 100-112.https://journal.unnes.ac.id/sju/index.php/jaist
15. Roostaee, S., & Ghaffary, H. R. (2016). Diagnosis of heart diseasebased on meta heuristic algorithms and clustering methods. Journal ofElectrical and Computer Engineering Innovations JECEI, 4(2), 105-110.https://doi.org/10.22061/jecei.2016.570
16. Sengur, A. (2008). An expert system based on principal componentanalysis, artificial immune system and fuzzy k-NN for diagnosis ofvalvular heart diseases. Computers in Biology and Medicine, 38(3), 329-338. https://doi.org/10.1016/j.compbiomed.2007.11.004
17. Seslier, T., & Karakuş, M. Ö. (2022). In healthcare applications ofmachine learning algorithms for prediction of heart ATTACKS. Journalof Scientific Reports-A, 051, 358-370.
18. Tama, B. A., Im, S., & Lee, S. (2020). Improving an IntelligentDetection System for Coronary Heart Disease Using a Two-TierClassifier Ensemble. BioMed Research International, 2020. https://doi.org/10.1155/2020/9816142
19. Tharwat, A., Mahdi, H., Elhoseny, M., & Hassanien, A. E. (2018).Recognizing human activity in mobile crowdsensing environmentusing optimized k-NN algorithm. Expert Systems with Applications,107, 32-44. https://doi.org/10.1016/j.eswa.2018.04.017
20. UPalani Teaching Fellow Professor, Ss. (2022). An IoT Enabled HeartDisease Monitoring System Using Grey Wolf Optimization and DeepBelief Network. https://doi.org/10.21203/rs.3.rs-1058279/v1
21. Wadhawan, S., & Maini, R. (2022). EBPSO: Enhanced binary particleswarm optimization for cardiac disease classification with featureselection. Expert Systems, 39(8), 1-20. https://doi.org/10.1111/exsy.13002
22. https://www.kaggle.com/datasets/johnsmith88/heart-disease-datasetAD: 02/08/2023
Volume 1, Issue 2, 2023
Page : 41-45