Evaluation of Performance of Feature Selection of Meta-Heuristic Optimization Methods in Medical Data

Yıl 2023, Cilt: IDAP-2023 : International Artificial Intelligence and Data Processing Symposium Sayı: IDAP-2023, 58 - 66, 18.10.2023

Hüseyin Gündoğdu , Osman Altay

https://doi.org/10.53070/bbd.1351629

Öz

In knowledge discovery, the processes of applying data cleaning, data integration, data selection-transformation, and data mining methods and obtaining meaningful information from the obtained patterns are performed, respectively. In recent years, it has become quite common to use metaheuristic optimization methods in the data selection phase. In this study, the nearest neighbor algorithm, support vector machine, and decision tree algorithms from machine learning algorithms were used on health data obtained from the University of California, Irvine. The whale optimization algorithm, salp swarm optimization, slime mould optimization, particle swarm optimization, and Harris Hawks optimization methods were used for feature selection. The obtained results were compared in detail.

Anahtar Kelimeler

Feature selection, meta-heuristic optimization algorithms, Whale optimization algorithm, Harris Hawks optimization

Tıp Verilerinde Meta-Sezgisel Optimizasyon Yöntemlerinin Özellik Seçimi Performanslarının Karşılaştırılması

Öz

Bilgi keşfinde sırasıyla veri temizleme, veri bütünleştirme, veri seçme-dönüştürme ve veri madenciliği yöntemlerini uygulama ve elde edilen örüntülerden anlamlı bilgiler elde etme süreçleri gerçekleştirilir. Son yıllarda veri seçimi aşamasında metasezgisel optimizasyon yöntemlerinin kullanılması oldukça yaygın hale gelmiştir. Bu çalışmada California Üniversitesi, Irvine'den elde edilen sağlık verileri üzerinde makine öğrenmesi algoritmalarından en yakın komşu algoritması, destek vektör makinesi ve karar ağacı algoritmaları kullanılmıştır. Özellik seçimi için balina optimizasyon algoritması, salp sürü optimizasyonu, slime küf optimizasyonu, parçacık sürü optimizasyonu ve Harris Hawks optimizasyon yöntemleri kullanılmıştır. Elde edilen sonuçlar ayrıntılı olarak karşılaştırılmıştır.

Anahtar Kelimeler

Özellik Seçimi, Meta-Sezgisel Optimizasyon Algoritmaları, Baline Optimizasyonu, Harris Şahini Optimizasyonu

Kaynakça

• Abualigah, L., Shehab, M., Alshinwan, M., & Alabool, H. (2020). Salp swarm algorithm: a comprehensive survey. Neural Computing and Applications, 32(15), 11195–11215.
• Altay, O., & Tezi, D. (2020). KENDİLİĞİNDEN YerleşenÇeli̇Li̇fli̇ Beton PerformansiniTahmi̇n EdeceSi̇stemi̇nModellenmesi̇.
• Altay, O., & Varol Altay, E. (2022). Investigation of Slime Mould Algorithm and Hybrid Slime Mould Algorithms’ Performance in Global Optimization Problems. DÜMF Mühendislik Dergisi, 4, 661–671.
• Baştanlar, Y., & Ozuysal, M. (2014). Introduction to Machine Learning Second Edition. In Methods in molecular biology (Clifton, N.J.) (Vol. 1107).
• Brereton, R. G., & Lloyd, G. R. (2010). Support Vector Machines for classification and regression. Analyst, 135(2), 230–267.
• Castellanos-garzón, J. A., Costa, E., Luis, J., Jaimes, S., & Corchado, J. M. (2019). Knowledge-Based Systems An evolutionary framework for machine learning applied to medical. Knowledge-Based Systems, 185, 104982.
• Cervantes, J., Garcia-Lamont, F., Rodríguez-Mazahua, L., & Lopez, A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408, 189–215.
• Chen, H., Li, C., Mafarja, M., Heidari, A. A., Chen, Y., & Cai, Z. (2023). Slime mould algorithm: a comprehensive review of recent variants and applications. International Journal of Systems Science, 54(1), 204–235. Chicco, D., & Jurman, G. (2020). Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone. BMC Medical Informatics and Decision Making, 20(1), 1–16. Dokeroglu, T., Deniz, A., & Kiziloz, H. E. (2022). A comprehensive survey on recent metaheuristics for feature selection. Neurocomputing, 494, 269–296.
• Faris, H., Mafarja, M. M., Heidari, A. A., Aljarah, I., Al-Zoubi, A. M., Mirjalili, S., & Fujita, H. (2018). An efficient binary Salp Swarm Algorithm with crossover scheme for feature selection problems. Knowledge-Based Systems, 154(March), 43–67.
• Gama, J. (2004). Functional trees. Machine Learning, 55(3), 219–250.
• Gharehchopogh, F. S., & Gholizadeh, H. (2019). A comprehensive survey: Whale Optimization Algorithm and its applications. Swarm and Evolutionary Computation, 48(March), 1–24.
• Hegazy, A. E., Makhlouf, M. A., & El-Tawel, G. S. (2020). Improved salp swarm algorithm for feature selection. Journal of King Saud University - Computer and Information Sciences, 32(3), 335–344.
• Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872.
• Iqbal, M., Setiawan, M. N., Irawan, M. I., Khalif, K. M. N. K., Muhammad, N., & Aziz, M. K. B. M. (2022). Cardiovascular disease detection from high utility rare rule mining. Artificial Intelligence in Medicine, 131.
• J R Quinlan. (1986). Induction of Decision Trees. Machine Learning, 1(1), 81–106.
• James Kennedy and Russell E. (2011). Particle Swarm Optimization. The Industrial Electronics Handbook - Five Volume Set, 1942–1948.
• Kononenko, I. (2001). Machine learning for medical diagnosis: History, state of the art and perspective. Artificial Intelligence in Medicine, 23(1), 89–109.
• Li, S., Chen, H., Wang, M., Heidari, A. A., & Mirjalili, S. (2020). Slime mould algorithm: A new method for stochastic optimization. Future Generation Computer Systems, 111, 300–323.
• Magoulas, G. D., & Prentza, A. (2001). Machine Learning in Medical Applications. 300–307.
• Mirjalili, S., Gandomi, A. H., Zahra, S., & Saremi, S. (2017). Advances in Engineering Software Salp Swarm Algorithm : A bio-inspired optimizer for engineering design problems. 114, 163–191.
• Mirjalili, S., & Lewis, A. (2016). Advances in Engineering Software The Whale Optimization Algorithm. 95, 51–67. Mitchell, T. M. (n.d.). Machine Learning.
• Nagarajan, S. M., Muthukumaran, V., Murugesan, R., Joseph, R. B., & Munirathanam, M. (2021). Feature selection model for healthcare analysis and classification using classifier ensemble technique. International Journal of Systems Assurance Engineering and Management.
• Nordin, N., Zainol, Z., Mohd Noor, M. H., & Chan, L. F. (2022). Suicidal behaviour prediction models using machine learning techniques: A systematic review. Artificial Intelligence in Medicine, 132(August), 102395.
• Peng, L., Cai, Z., Heidari, A. A., Zhang, L., & Chen, H. (2023). Hierarchical Harris hawks optimizer for feature selection. Journal of Advanced Research, xxxx.
• Selvakuberan, K., Kayathiri, D., Harini, B., & Devi, M. I. (2011). An efficient feature selection method for classification in health care systems using machine learning techniques. ICECT 2011 - 2011 3rd International Conference on Electronics Computer Technology, 4, 223–226.
• Shailaja, K., & Scholar, M. T. (2018). Machine Learning in Healthcare : A Review. Iceca, 9–13.
• Suparyanto dan Rosad (2015. (2020). Data Mining: Practical Machine Learning Tools and Tecniques. In Suparyanto dan Rosad (2015 (Vol. 5, Issue 3).
• Tanyıldızı, E., & Cigali, T. (2017). Kaotik Haritalı Balina Optimizasyon Algoritmaları. Fırat Üniversitesi Mühendislik Bilimleri Dergisi, 29(1), 307–317.
• Taylor, R. A., Moore, C. L., Cheung, K., & Brandt, C. (2018). Predicting urinary tract infections in the emergency department with machine learning. Plus One, 3, 1–15.
• Tsanas, A., Little, M. A., Fox, C., & Ramig, L. O. (2014). Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 22(1), 181–190.
• Xiong, Y., Lin, L., Chen, Y., Salerno, S., Li, Y., Zeng, X., & Li, H. (2022). Prediction of gestational diabetes mellitus in the first 19 weeks of pregnancy using machine learning techniques. Artificial Intelligence in Medicine.
• Xue, B., Zhang, M., & Browne, W. N. (2013). Particle swarm optimization for feature selection in classification: A multi-objective approach. IEEE Transactions on Cybernetics, 43(6), 1656–1671.
• Yuan, Z., Chen, B., Liu, J., Chen, H., Peng, D., & Li, P. (2023). Anomaly detection based on weighted fuzzy-rough density. Applied Soft Computing, 134, 109995.
• Zhang, X., Wu, H., Chen, T., & Wang, G. (2022). Automatic diagnosis of arrhythmia with electrocardiogram using multiple instance learning: From rhythm annotation to heartbeat prediction. Artificial Intelligence in Medicine, 132(August), 102379.
• Zheng, Y., Li, Y., Wang, G., Chen, Y., Xu, Q., Fan, J., & Cui, X. (2019). A Novel Hybrid Algorithm for Feature Selection Based on Whale Optimization Algorithm. IEEE Access, 7, 14908–14923.