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Koroner Arter Hastalığı Sınıflandırılmasında Destek Vektör Makinelerinin Gri Kurt Optimizasyonuna Dayalı Özellik Seçim Yöntemi ile Geliştirilmesi

Year 2024, Volume: 5 Issue: 1, 37 - 44

Büşra Er Ugur Fidan

https://doi.org/10.53608/estudambilisim.1409734

Abstract

Makine öğrenmesi yöntemleri, büyük veri kümelerinin analiz edilmesine olanak sağlayarak koroner arter rahatsızlığı ve/veya buna benzer hastalık ve durumların tespit edilmesinde kullanılan etkili bir araçtır. Büyük veri kümelerinde işlem hızını ve sınıflandırma başarımını etkileyen gereksiz veya kararı olumsuz yönde etkileyen veriler bulunabilmektedir. Özellik seçim tekniklerinin uygulanması gereksiz verilerin ortadan kaldırılmasına olanak sağlamaktadır. Bu çalışmada, koroner arter hastalığını teşhis etmek amacıyla en uygun özellik alt kümesini belirlemek üzere yeni bir sınıflandırma yöntemi önerilmiştir. Önerilen yöntem, öznitelik seçimi ve sınıflandırma olmak üzere iki ana aşamadan oluşmaktadır. Önerilen yöntemin performans doğrulaması için Cleveland kalp hastalığı veri seti kullanılmıştır. İlk aşamada, en iyi özellikleri bulmak için gri kurt optimizasyonu (GWO) kullanılmıştır. Kullanılan veri setinde bulunan 13 parametre arasında 7 en etkili parametre seçilmiş ve sınıflandırma işlemi bu 7 parametre üzerinden gerçekleştirilmiştir. İkinci aşamada, GWO'nun uygunluk fonksiyonu, destek vektör makinesi (SVM) sınıflandırıcısı kullanılarak değerlendirilmiştir. Çalışmada belirlenen uygunluk fonksiyonları SVM’de kullanılan çekirdek matrislerin farklı varyasyonları ile değerlendirilmiştir. Bu aşamada en yüksek doğruluk elde edilen çekirdek matris belirlenmiştir. Deneysel sonuçlar, önerilen GWO-SVM'nin lineer çekirdek matris kullanılarak %95.91 doğrulukta, %95.64 duyarlılıkta ve %91.66 başarı ile mevcut çalışmalara kıyasla daha yüksek başarım sağlandığını göstermiştir

Keywords

Gri Kurt Optimizasyonu Öznitelik Çıkarımı Destek Vektör Makinesi Koroner Arter Hastalığı Çekirdek Matris

Ethical Statement

Bu araştırma, planlamadan uygulamaya, veri toplama sürecinden veri analizine kadar tüm aşamalarda "Yükseköğretim Kurumları Bilimsel Araştırma ve Yayın Etiği Yönergesi" çerçevesinde belirlenen kurallara uygunluk göstermiştir. Yönergenin ikinci bölümü olan "Bilimsel Araştırma ve Yayın Etiğine Aykırı Eylemler" başlığı altındaki kurallarda herhangi bir ihlal gerçekleşmemiştir. Çalışmanın yazım sürecinde bilimsel etik ve alıntı kurallarına tam olarak uyulmuş, toplanan veriler üzerinde herhangi bir manipülasyon yapılmamış, ayrıca bu çalışma, başka herhangi bir akademik yayın ortamında değerlendirme için gönderilmemiştir.

Supporting Institution

Yazarlar, bu çalışma için kamu, ticari veya kâr amacı gütmeyen sektörlerdeki fon kuruluşlarından özel bir hibe almadıklarını beyan ederler.

Thanks

Yazarlar olarak, bu çalışma ile ilgili herhangi bir kişi veya kurumla çıkar çatışması bulunmadığını onaylıyoruz.

References

  • Shouman, M., Turner, T. & Stocker, R. 2012. Using data mining techniques in heart disease diagnosis and treatment. Japan-Egypt Conference on Electronics, Communications and Computers, 173–177. DOI: 10.1109/JEC-ECC.2012.6186978
  • Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. 2022. Machine learning in medical applications: A review of state-of-the-art methods. Computers in Biology and Medicine, 145, 105458. DOI: 10.1016/j.compbiomed.2022.105458
  • Ahsan, M. M., Siddique, Z., 2022. Machine learning-based heart disease diagnosis: A systematic literature review. Artificial Intelligence in Medicine, 128, 102289. DOI: 10.1016/j.artmed.2022.102289
  • Averbuch, T., Sullivan, K., Sauer, A., Mamas, M. A., Voors, A. A., Gale, C. P., Van Spall, H. G. 2022. Applications of artificial intelligence and machine learning in heart failure. European Heart Journal-Digital Health, 3(2), 311-322. DOI: 10.1093/ehjdh/ztac025
  • Ramesh, T. R., Lilhore, U. K., Poongodi, M., Simaiya, S., Kaur, A., Hamdi, M. 2022. Predictive analysis of heart diseases with machine learning approaches. Malaysian Journal of Computer Science, 132-148. DOI: 10.22452/mjcs.sp2022no1.10
  • Taha, B., Liza, F. R., Masud, M. A., Bepery, C., Islam, M. T., Samsuzzaman, M. 2023. BrainVisionNet: A Deep Learning-based approach to evaluate the potential of microwave ımaging for classification of brain tumors. In 2023 International Conference on Next-Generation Computing, IoT and Machine Learning, 1-6. DOI: 10.3390/healthcare9020153
  • Arabahmadi, M., Farahbakhsh, R., Rezazadeh, J. 2022. Deep learning for smart Healthcare-A survey on brain tumor detection from medical imaging. Sensors, 22(5). DOI: 10.3390/s22051960
  • Shoeibi, A., Khodatars, M., Jafari, M., Ghassemi, N., Moridian, P., Alizadesani, R., Gorriz, J. M. 2022. Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review. Information Fusion. DOI: 10.1016/j.inffus.2022.12.010
  • Mazhar, T., Haq, I., Ditta, A., Mohsan, S. A. H., Rehman, F., Zafar, I., Goh, L. P. W. 2023. The role of machine learning and deep learning approaches for the detection of skin cancer. In Healthcare, 11(3), 415. DOI: 10.3390/healthcare11030415
  • Tembhurne, J. V., Hebbar, N., Patil, H. Y., Diwan, T. 2023. Skin cancer detection using ensemble of machine learning and deep learning techniques. Multimedia Tools and Applications, 1-24. DOI: 10.1007/s11042-023-14697-3
  • Humayun, M., Khalil, M. I., Almuayqil, S. N., Jhanjhi, N. Z. 2023. Framework for detecting breast cancer risk presence using deep learning. Electronics, 12(2), 403. DOI: 10.3390/electronics12020403
  • Krishnaiah, V., Narsimha, G., Chandra, N.S. (2015) Heart disease prediction system using data mining technique by fuzzy K-NN approach. Emerging ICT for Bridging the FutureProceedings of the 49th Annual Convention of the Computer Society of India (CSI), 1, 371–384. DOI: 10.1007/978-3-319-13728-5_42
  • Libby, P., Theroux, P. 2005. Pathophysiology of coronary artery disease. Circulation, 111(25), 3481-3488. DOI: 10.1161/CIRCULATIONAHA.105.537878
  • Abdar, M., Książek, W., Acharya, U. R., Tan, R. S., Makarenkov, V., Pławiak, P. 2019. A new machine learning technique for an accurate diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 179, 104992. DOI: 10.1016/j.cmpb.2019.104992
  • Al-Tashi, Q., Rais, H., Jadid, S. 2019. Feature selection method based on grey wolf optimization for coronary artery disease classification. In Recent Trends in Data Science and Soft Computing: Proceedings of the 3rd International Conference of Reliable Information and Communication Technology, 257-266. DOI: 10.1007/978-3-319-99007-1_25
  • Tama, B. A., Im, S., Lee, S. 2020. Improving an intelligent detection system for coronary heart disease using a two-tier classifier ensemble. BioMed Research International, 2020. DOI: 10.1155/2020/9816142
  • Moturi, S., Rao, S. T., Vemuru, S. 2021. Grey wolf assisted dragonfly-based weighted rule generation for predicting heart disease and breast cancer. Computerized Medical Imaging and Graphics, 91, 101936. DOI: 10.1016/j.compmedimag.2021.101936
  • Le, T.M., Pham, T.N., Dao, S.V. 2021. A novel wrapper-based feature selection for heart failure prediction using an adaptive particle swarm grey wolf optimization. Enhanced Telemedicine and e-Health: Advanced IoT Enabled Soft Computing Framework, 315-336. DOI: 10.1007/978-3-030-70111-6_15
  • Deepika, D., Balaji, N. 2022. Effective heart disease prediction with Grey-wolf with Firefly algorithm-differential evolution (GF-DE) for feature selection and weighted ANN classification. Computer Methods in Biomechanics and Biomedical Engineering, 25, 1409 - 1427. DOI: 10.1080/10255842.2022.2078966
  • Krishna, E. R., Devarakonda, N. 2023. Feature selection method based on GWO-PSO for coronary artery disease classification. Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies. 1-8. DOI: 10.1109/ICAECT57570.2023.10118351
  • Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., Yarifard, A.A. 2017. Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput. Methods Programs Biomed. 141, 19–26 DOI: 10.1016/j.cmpb.2017.01.004
  • Paul, A.K., Shill, P.C., Rabin, M.R.I., Akhand, M. A. H. 2016. Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease. 2016 5th International Conference on Informatics, Electronics and Vision, 145–150 DOI: 10.1109/ICIEV.2016.7759984
  • Subanya, B., Rajalaxmi, R. R. 2014. Feature selection using artificial bee colony for cardiovascular disease classification. 2014 International Conference on Electronics and Communication Systems. DOI: 10.1109/ECS.2014.6892729
  • Mirjalili, S., Mirjalili, S. M., Lewis, A. 2014. Grey wolf optimizer. Advances in engineering software, 69, 46-61. DOI: 10.1016/j.advengsoft.2013.12.007
  • Lanckriet, G. R., Cristianini, N., Bartlett, P., Ghaoui, L. E., Jordan, M. I. 2004. Learning the kernel matrix with semidefinite programming. Journal of Machine learning research, 27-72.
  • Weinberger, K. Q., Sha, F., Saul, L. K. 2004. Learning a kernel matrix for nonlinear dimensionality reduction. In Proceedings of the twenty-first international conference on Machine learning (106). DOI: 10.1145/1015330.1015345
  • Huang, T. M., Kecman, V., Kopriva, I. 2006. Kernel based algorithms for mining huge data sets (1). DOI: 10.1007/3-540-31689-2
  • Takeda, H., Farsiu, S., Milanfar, P. 2007. Kernel regression for image processing and reconstruction. IEEE Transactions on image processing, 16(2), 349-366. DOI: 10.1109/tip.2006.888330
  • Zhang, K., Lan, L., Wang, Z., Moerchen, F. 2012. Scaling up kernel SVM on limited resources: A low-rank linearization approach. In Artificial intelligence and statistics, 1425-1434.
  • Astuti, W., Fadli, A., Tan, S., Akmeliawati, R. 2019. Brain signal recognition system based on One-Against-One Multiclass Support Vector Machines: a comparison with Multiclass Neural Network. In Journal of Physics: Conference Series, 1367(1), 012027. DOI: 10.1088/1742-6596/1367/1/012027
  • Cengil, E., Çınar, A. 2020. Göğüs verileri metrikleri üzerinden kanser sınıflandırılması. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11(2), 513-519. DOI: 10.24012/dumf.578606

Development of Support Vector Machines in The Classification of Coronary Artery Disease with Gray Wolf Optimization Based Feature Selection Method

Year 2024, Volume: 5 Issue: 1, 37 - 44

Büşra Er Ugur Fidan

https://doi.org/10.53608/estudambilisim.1409734

Abstract

Machine learning methods are an effective tool used to detect coronary artery disease and/or similar diseases and conditions by allowing the analysis of large data sets. In large data sets, there may be unnecessary data that affects processing speed and classification performance or negatively affects the decision. Applying feature selection techniques allows the elimination of unnecessary data. In this study, a new classification method is proposed to determine the most appropriate feature subset to diagnose coronary artery disease. The proposed method consists of two main stages: feature selection and classification. Cleveland heart disease dataset was used for performance validation of the proposed method. In the first stage, gray wolf optimization (GWO) was used to find the best features. Among the 13 parameters in the data set used, 7 most effective parameters were selected and the classification process was carried out on these 7 parameters. In the second stage, the fitness function of GWO was evaluated using the support vector machine (SVM) classifier. The fitness functions determined in the study were evaluated with different variations of the kernel matrices used in SVM. At this stage, the core matrix with the highest accuracy was determined. Experimental results showed that the proposed GWO-SVM achieved higher performance compared to existing studies, with 95.91% accuracy, 95.64% sensitivity and 91.66% success using the linear kernel matrix.

Keywords

Grey Wolf Optimization Future Selection Support Vector Machine Coronary Artery Disease Kernel Matrix

Ethical Statement

This research has complied with the rules determined within the framework of the "Higher Education Institutions Scientific Research and Publication Ethics Directive" at all stages from planning to implementation, from data collection process to data analysis. There has been no violation of the rules under the heading "Actions Against Scientific Research and Publication Ethics", which is the second part of the Directive. In the writing process of the study, the scientific ethics and citation rules were fully followed, no manipulation was performed on the collected data, and this study was not sent for evaluation in any other academic publication environment.

Supporting Institution

Authors declare that they have not received a special grant for this work from funding organizations in the public, commercial or non-profit sectors.

Thanks

As authors, we confirm that there is no conflict of interest with any person or institution related to this work.

References

  • Shouman, M., Turner, T. & Stocker, R. 2012. Using data mining techniques in heart disease diagnosis and treatment. Japan-Egypt Conference on Electronics, Communications and Computers, 173–177. DOI: 10.1109/JEC-ECC.2012.6186978
  • Shehab, M., Abualigah, L., Shambour, Q., Abu-Hashem, M. A., Shambour, M. K. Y., Alsalibi, A. I., & Gandomi, A. H. 2022. Machine learning in medical applications: A review of state-of-the-art methods. Computers in Biology and Medicine, 145, 105458. DOI: 10.1016/j.compbiomed.2022.105458
  • Ahsan, M. M., Siddique, Z., 2022. Machine learning-based heart disease diagnosis: A systematic literature review. Artificial Intelligence in Medicine, 128, 102289. DOI: 10.1016/j.artmed.2022.102289
  • Averbuch, T., Sullivan, K., Sauer, A., Mamas, M. A., Voors, A. A., Gale, C. P., Van Spall, H. G. 2022. Applications of artificial intelligence and machine learning in heart failure. European Heart Journal-Digital Health, 3(2), 311-322. DOI: 10.1093/ehjdh/ztac025
  • Ramesh, T. R., Lilhore, U. K., Poongodi, M., Simaiya, S., Kaur, A., Hamdi, M. 2022. Predictive analysis of heart diseases with machine learning approaches. Malaysian Journal of Computer Science, 132-148. DOI: 10.22452/mjcs.sp2022no1.10
  • Taha, B., Liza, F. R., Masud, M. A., Bepery, C., Islam, M. T., Samsuzzaman, M. 2023. BrainVisionNet: A Deep Learning-based approach to evaluate the potential of microwave ımaging for classification of brain tumors. In 2023 International Conference on Next-Generation Computing, IoT and Machine Learning, 1-6. DOI: 10.3390/healthcare9020153
  • Arabahmadi, M., Farahbakhsh, R., Rezazadeh, J. 2022. Deep learning for smart Healthcare-A survey on brain tumor detection from medical imaging. Sensors, 22(5). DOI: 10.3390/s22051960
  • Shoeibi, A., Khodatars, M., Jafari, M., Ghassemi, N., Moridian, P., Alizadesani, R., Gorriz, J. M. 2022. Diagnosis of brain diseases in fusion of neuroimaging modalities using deep learning: A review. Information Fusion. DOI: 10.1016/j.inffus.2022.12.010
  • Mazhar, T., Haq, I., Ditta, A., Mohsan, S. A. H., Rehman, F., Zafar, I., Goh, L. P. W. 2023. The role of machine learning and deep learning approaches for the detection of skin cancer. In Healthcare, 11(3), 415. DOI: 10.3390/healthcare11030415
  • Tembhurne, J. V., Hebbar, N., Patil, H. Y., Diwan, T. 2023. Skin cancer detection using ensemble of machine learning and deep learning techniques. Multimedia Tools and Applications, 1-24. DOI: 10.1007/s11042-023-14697-3
  • Humayun, M., Khalil, M. I., Almuayqil, S. N., Jhanjhi, N. Z. 2023. Framework for detecting breast cancer risk presence using deep learning. Electronics, 12(2), 403. DOI: 10.3390/electronics12020403
  • Krishnaiah, V., Narsimha, G., Chandra, N.S. (2015) Heart disease prediction system using data mining technique by fuzzy K-NN approach. Emerging ICT for Bridging the FutureProceedings of the 49th Annual Convention of the Computer Society of India (CSI), 1, 371–384. DOI: 10.1007/978-3-319-13728-5_42
  • Libby, P., Theroux, P. 2005. Pathophysiology of coronary artery disease. Circulation, 111(25), 3481-3488. DOI: 10.1161/CIRCULATIONAHA.105.537878
  • Abdar, M., Książek, W., Acharya, U. R., Tan, R. S., Makarenkov, V., Pławiak, P. 2019. A new machine learning technique for an accurate diagnosis of coronary artery disease. Computer methods and programs in biomedicine, 179, 104992. DOI: 10.1016/j.cmpb.2019.104992
  • Al-Tashi, Q., Rais, H., Jadid, S. 2019. Feature selection method based on grey wolf optimization for coronary artery disease classification. In Recent Trends in Data Science and Soft Computing: Proceedings of the 3rd International Conference of Reliable Information and Communication Technology, 257-266. DOI: 10.1007/978-3-319-99007-1_25
  • Tama, B. A., Im, S., Lee, S. 2020. Improving an intelligent detection system for coronary heart disease using a two-tier classifier ensemble. BioMed Research International, 2020. DOI: 10.1155/2020/9816142
  • Moturi, S., Rao, S. T., Vemuru, S. 2021. Grey wolf assisted dragonfly-based weighted rule generation for predicting heart disease and breast cancer. Computerized Medical Imaging and Graphics, 91, 101936. DOI: 10.1016/j.compmedimag.2021.101936
  • Le, T.M., Pham, T.N., Dao, S.V. 2021. A novel wrapper-based feature selection for heart failure prediction using an adaptive particle swarm grey wolf optimization. Enhanced Telemedicine and e-Health: Advanced IoT Enabled Soft Computing Framework, 315-336. DOI: 10.1007/978-3-030-70111-6_15
  • Deepika, D., Balaji, N. 2022. Effective heart disease prediction with Grey-wolf with Firefly algorithm-differential evolution (GF-DE) for feature selection and weighted ANN classification. Computer Methods in Biomechanics and Biomedical Engineering, 25, 1409 - 1427. DOI: 10.1080/10255842.2022.2078966
  • Krishna, E. R., Devarakonda, N. 2023. Feature selection method based on GWO-PSO for coronary artery disease classification. Third International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies. 1-8. DOI: 10.1109/ICAECT57570.2023.10118351
  • Arabasadi, Z., Alizadehsani, R., Roshanzamir, M., Moosaei, H., Yarifard, A.A. 2017. Computer aided decision making for heart disease detection using hybrid neural network-Genetic algorithm. Comput. Methods Programs Biomed. 141, 19–26 DOI: 10.1016/j.cmpb.2017.01.004
  • Paul, A.K., Shill, P.C., Rabin, M.R.I., Akhand, M. A. H. 2016. Genetic algorithm based fuzzy decision support system for the diagnosis of heart disease. 2016 5th International Conference on Informatics, Electronics and Vision, 145–150 DOI: 10.1109/ICIEV.2016.7759984
  • Subanya, B., Rajalaxmi, R. R. 2014. Feature selection using artificial bee colony for cardiovascular disease classification. 2014 International Conference on Electronics and Communication Systems. DOI: 10.1109/ECS.2014.6892729
  • Mirjalili, S., Mirjalili, S. M., Lewis, A. 2014. Grey wolf optimizer. Advances in engineering software, 69, 46-61. DOI: 10.1016/j.advengsoft.2013.12.007
  • Lanckriet, G. R., Cristianini, N., Bartlett, P., Ghaoui, L. E., Jordan, M. I. 2004. Learning the kernel matrix with semidefinite programming. Journal of Machine learning research, 27-72.
  • Weinberger, K. Q., Sha, F., Saul, L. K. 2004. Learning a kernel matrix for nonlinear dimensionality reduction. In Proceedings of the twenty-first international conference on Machine learning (106). DOI: 10.1145/1015330.1015345
  • Huang, T. M., Kecman, V., Kopriva, I. 2006. Kernel based algorithms for mining huge data sets (1). DOI: 10.1007/3-540-31689-2
  • Takeda, H., Farsiu, S., Milanfar, P. 2007. Kernel regression for image processing and reconstruction. IEEE Transactions on image processing, 16(2), 349-366. DOI: 10.1109/tip.2006.888330
  • Zhang, K., Lan, L., Wang, Z., Moerchen, F. 2012. Scaling up kernel SVM on limited resources: A low-rank linearization approach. In Artificial intelligence and statistics, 1425-1434.
  • Astuti, W., Fadli, A., Tan, S., Akmeliawati, R. 2019. Brain signal recognition system based on One-Against-One Multiclass Support Vector Machines: a comparison with Multiclass Neural Network. In Journal of Physics: Conference Series, 1367(1), 012027. DOI: 10.1088/1742-6596/1367/1/012027
  • Cengil, E., Çınar, A. 2020. Göğüs verileri metrikleri üzerinden kanser sınıflandırılması. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 11(2), 513-519. DOI: 10.24012/dumf.578606
There are 31 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Research Articles
Authors

Büşra Er 0000-0001-9682-8651

Ugur Fidan 0000-0003-0356-017X

Early Pub Date March 12, 2024
Publication Date
Submission Date January 2, 2024
Acceptance Date March 12, 2024
Published in Issue Year 2024 Volume: 5 Issue: 1

Cite

IEEE B. Er and U. Fidan, “Koroner Arter Hastalığı Sınıflandırılmasında Destek Vektör Makinelerinin Gri Kurt Optimizasyonuna Dayalı Özellik Seçim Yöntemi ile Geliştirilmesi”, Journal of ESTUDAM Information, vol. 5, no. 1, pp. 37–44, 2024, doi: 10.53608/estudambilisim.1409734.

Journal of ESTUDAM Information is indexed by Index Copernicus, Google Scholar, ASOS Index and ROAD index.