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THE EFFECT OF GENETIC ALGORITHM BASED FEATURE SELECTION ON DETECTION OF ANAEMIA DISEASE

Year 2025, Volume: 28 Issue: 1, 309 - 321, 03.03.2025

Mehtap Öklü , Hasan Badem

Abstract

Anaemia, which occurs when the body’s oxygen needs cannot be met for various reasons, has been reported by the World Health Organization to be seen in more than 500 million people in 2023. Additionally, the anemia is the most common blood disease in the world. One of the most important precautions for this disease is early diagnosis. In the literature, machine learning models have been proposed for achieving rapid and accurate diagnostic results. However, machine learning models may not always provide the desired level. Feature selection utilized with optimization algorithms can enhance the accuracy rates of machine learning models. In this study, improving the accuracy of anemia detection has been aimed on patients’ test results through a genetic algorithm as a feature selection operator. Genetic algorithm has been performed as feature selection to improve the classification performance of machine learning methods including k-nearest neighbors, naïve bayes, decision trees, logistic regression, and support vector machine. In the experimental results, higher accuracy rates have been obtained by the proposed method compared with obtained results without feature selection.

Keywords

anemia machine learning genetic algorithm feature selection

References

  • Acar, E. B., Karabey, C., & Köse, B. İnsansiz Hava Araci İle Paket Dağitiminda Gezgin Satici Probleminin Genetik Ve Parçacik Sürü Optimizasyon Algoritmalari İle Çözümü. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 10(20), 168-181. https://doi.org/10.54365/adyumbd.1249391
  • Ahmad, A., Alzaidi, K., Sari, M., & Uslu, H. (2023). Prediction of anemia with a particle swarm optimization-based approach. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 13(2). 10.11121/ijocta.2023.1269
  • Ajder, A. (2023). Geleneksel Yaklaşimlar: Parçacik Sürü Optimizasyonu Algoritmasi. Teknobilim-2023: Yapay Zeka ve Mühendislik, 21.
  • Alam, T., Qamar, S., Dixit, A., & Benaida, M. (2020). Genetic algorithm: Reviews, implementations, and applications. arXiv preprint arXiv:2007.12673. https://doi.org/10.48550/arXiv.2007.12673
  • Alp, G., & Soygazi, F. (2024, May). Meta-Heuristic Supported Feature Selection in Classification Algorithms for Diabetes Diagnosis. In 2024 32nd Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. 10.1109/SIU61531.2024.10601062
  • Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., & Ridella, S. (2012, April). The'K'in K-fold Cross Validation. In ESANN (Vol. 102, pp. 441-446).
  • Appiahene, P., Asare, J. W., Donkoh, E. T., Dimauro, G., & Maglietta, R. (2023). Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms. BioData mining, 16(1), 2. https://doi.org/10.1186/s13040-023-00319-z
  • Badem, H., Basturk, A., Caliskan, A., & Yuksel, M. E. (2017). A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited–memory BFGS optimization algorithms. Neurocomputing, 266, 506-526. https://doi.org/10.1016/j.neucom.2017.05.061
  • Badem, H. (2019). Parkinson Hastaliğinin Ses Sinyalleri Üzerinden Makine Öğrenmesi Teknikleri ile Tanimlanmasi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 8(2), 630-637. 10.28948/ngumuh.524658
  • Cheng, L., Nie, Y., Wen, H., Li, Y., Zhao, Y., Zhang, Q., ... & Fu, S. (2024). An ensemble machine learning model for predicting one-year mortality in elderly coronary heart disease patients with anemia. Journal of Big Data, 11(1), 1-20. https://doi.org/10.1186/s40537-024-00966-x
  • Eke, İ. (2022). Optimum PID Kazançları Genetik Algoritma İle Hesaplanan Otomatik Gerilim Regülatörü. International Journal of Engineering Research and Development, 14(3), 351-361. https://doi.org/10.29137/umagd.1176936
  • Gizzi, E., Nair, L., Chernova, S., & Sinapov, J. (2022). Creative problem solving in artificially intelligent agents: A survey and framework. Journal of Artificial Intelligence Research, 75, 857-911. https://doi.org/10.1613/jair.1.13864
  • Gülmez, B. (2023). Market zinciri ürün dağıtımı probleminin farklı genetik algoritma versiyonları ile çözümü ve karşılaştırması. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 180-196. Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.
  • Inneci, T., & Badem, H. (2023). Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network. Bioengineering, 10(6), 639. https://doi.org/10.3390/bioengineering10060639
  • Kaleli, S. S. (2023). Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma ile Analizi ve Optimum Portföy Oluşturma. MANAS Sosyal Araştırmalar Dergisi, 12(2), 557-565. https://doi.org/10.33206/mjss.1215054
  • Karaboğa, D. (2020). Yapay zeka optimizasyon algoritmaları (7.Baskı). Nobel Akademi Yayıncılık.
  • Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: past, present, and future. Multimedia tools and applications, 80, 8091-8126. https://doi.org/10.1007/s11042-020-10139-6
  • Keklik, G., & Özcan, B. D. (2023). Genetik Algoritmaların İşleyişi ve Genetik Algoritma Uygulamalarında Kullanılan Operatörler. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 1052-1066. https://doi.org/10.47495/okufbed.1161413
  • Khan, J. R., Chowdhury, S., Islam, H., & Raheem, E. (2019). Machine learning algorithms to predict the childhood anemia in Bangladesh. Journal of Data Science, 17(1), 195-218. 10.6339/JDS.201901_17(1).0009
  • Lambora, A., Gupta, K., & Chopra, K. (2019, February). Genetic algorithm-A literature review. In 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 380-384). IEEE, 10.1109/COMITCon.2019.8862255
  • Mirjalili, S., Song Dong, J., Sadiq, A. S., & Faris, H. (2020). Genetic algorithm: Theory, literature review, and application in image reconstruction. Nature-inspired optimizers: Theories, literature reviews and applications, 69-85. https://doi.org/10.1007/978-3-030-12127-3_5
  • Narayan, Y. (2021). Comparative analysis of SVM and Naive Bayes classifier for the SEMG signal classification. Materials Today: Proceedings, 37, 3241-3245. https://doi.org/10.1016/j.matpr.2020.09.093
  • Öklü, M., & Canbay, P. (2023). Makine Öğrenmesi Yöntemleri ile Şehirlerin Hava Kalitesi Tahmini. International Journal of Advances in Engineering and Pure Sciences, 35(1), 39-53. https://doi.org/10.7240/jeps.1175507
  • Pertiwi, D. A. A., Ahmad, K., Salahudin, S. N., Annegrat, A. M., & Muslim, M. A. (2024). Using genetic algorithm feature selection to optimize XGBoost performance in Australian credit. Journal of Soft Computing Exploration, 5(1), 92-98. https://doi.org/10.52465/joscex.v5i1.302
  • Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883. 10.4249/scholarpedia.1883
  • Saputra, D. C. E., Sunat, K., & Ratnaningsih, T. (2023, February). A new artificial intelligence approach using extreme learning machine as the potentially effective model to predict and analyze the diagnosis of anemia. In Healthcare (Vol. 11, No. 5, p. 697). MDPI. https://doi.org/10.3390/healthcare11050697
  • Seymen, V. (2014). Demir eksikliği anemisi hastalığının tespitinde kullanılan sınıflandırma algoritmalarının karşılaştırılması (Master's thesis, Sakarya Universitesi (Turkey)).
  • Shukla, A., Pandey, H. M., & Mehrotra, D. (2015, February). Comparative review of selection techniques in genetic algorithm. In 2015 international conference on futuristic trends on computational analysis and knowledge management (ABLAZE) (pp. 515-519). IEEE. 10.1109/ABLAZE.2015.7154916
  • Taşcı, E., & Onan, A. (2016). K-en yakın komşu algoritması parametrelerinin sınıflandırma performansı üzerine etkisinin incelenmesi. Akademik Bilişim, 1(1), 4-18.
  • Türkkahraman, Ş. M., & Karabulut, K. (2023). Sosyal Ağ Varlığında Takım Oluşturma Problemine Hibrit Bir Genetik Algoritma Önerisi. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 25(73), 181-192. https://doi.org/10.21205/deufmd.2023257315
  • Vohra, R., Dudyala, A. K., Pahareeya, J., & Hussain, A. (2022). Decision rules generation using decision tree classifier and their optimization for anemia classification. In Inventive Computation and Information Technologies: Proceedings of ICICIT 2021 (pp. 721-737). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-16-6723-7_53
  • Wan, H. (2024). Applying the genetic algorithm to optimization problems. WIT Transactions on Information and Communication Technologies, 2. 10.2495/AIENG930332
  • Weile, D. S., & Michielssen, E. (1997). Genetic algorithm optimization applied to electromagnetics: A review. IEEE Transactions on Antennas and Propagation, 45(3), 343-353. 10.1109/8.558650
  • World Health Organization (WHO) (2008). Worldwide prevalence of anaemia 1993-2005: WHO global database on anaemia.
  • World Health Organization (WHO). (2014). Global nutrition targets 2025: Stunting policy brief (No. WHO/NMH/NHD/14.3).
  • World Health Organization (WHO). (2024). Guideline on haemoglobin cutoffs to define anaemia in individuals and populations. World Health Organization.
  • Yağmur, N., Temurtaş, H., & Dağ, İ. (2023). Anemi Hastaliğinin Yapay Sinir Ağlari Yöntemleri Kullanilarak Siniflandirilmasi. Journal of Scientific Reports-B, (008), 20-34.
  • Yıldız, T. K., Yurtay, N., & Öneç, B. (2021). Classifying anemia types using artificial learning methods. Engineering Science and Technology, an International Journal, 24(1), 50-70. https://doi.org/10.1016/j.jestch.2020.12.003
  • Zhang, S., & Li, J. (2021). KNN classification with one-step computation. IEEE Transactions on Knowledge and Data Engineering, 35(3), 2711-2723. 10.1109/TKDE.2021.3119140

GENETİK ALGORİTMA TEMELLİ ÖZNİTELİK SEÇİMİNİN ANEMİ HASTALIĞININ TESPİTİNE ETKİSİ

Year 2025, Volume: 28 Issue: 1, 309 - 321, 03.03.2025

Mehtap Öklü , Hasan Badem

Abstract

Vücuttaki oksijen ihtiyacının farklı sebeplerle karşılanamaması durumunda ortaya çıkan anemi, 2023’de Dünya Sağlık Örgütü 500 milyondan fazla kişide görüldüğünü rapor etmiştir. Ayrıca, anemi dünyada en sık görülen kan hastalığıdır. Bu hastalığın en önemli önlemlerinden biri erken teşhistir. Literatürde teşhis konusunda hızlı ve başarılı sonuçların elde edilebilmesi için makine öğrenmesi modelleri önerilmektedir. Ancak makine öğrenmesi modelleri arzu edilen düzeyde etkin sonuçlar veremeyebilir. Optimizasyon algoritmaları ile gerçekleştirilen öznitelik seçimi, makine öğrenmesi modellerinin başarı oranlarını arttırabilmektedir. Bu çalışmada hastaların tahlil sonuçları üzerinden gerçekleştirilen anemi tespitinin başarı oranını, öznitelik seçici olarak genetik algoritma ile artırılması amaçlanmıştır. K-en yakın komşu, naive bayes, karar ağaçları, lojistik regresyon ve destek vektör makinesi makine öğrenmesi yöntemlerinin sınıflandırma başarımını arıtmak için genetik algoritma ile öznitelik seçimi gerçekleştirilmiştir. Elde edilen deneysel sonuçlarda önerilen yöntem ile öznitelik seçimi yapılmadan elde edilen sonuçlara göre daha yüksek doğruluk oranları elde edilmiştir.

Keywords

anemi makine öğrenmesi genetik algoritma öznitelik seçimi

References

  • Acar, E. B., Karabey, C., & Köse, B. İnsansiz Hava Araci İle Paket Dağitiminda Gezgin Satici Probleminin Genetik Ve Parçacik Sürü Optimizasyon Algoritmalari İle Çözümü. Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi, 10(20), 168-181. https://doi.org/10.54365/adyumbd.1249391
  • Ahmad, A., Alzaidi, K., Sari, M., & Uslu, H. (2023). Prediction of anemia with a particle swarm optimization-based approach. An International Journal of Optimization and Control: Theories & Applications (IJOCTA), 13(2). 10.11121/ijocta.2023.1269
  • Ajder, A. (2023). Geleneksel Yaklaşimlar: Parçacik Sürü Optimizasyonu Algoritmasi. Teknobilim-2023: Yapay Zeka ve Mühendislik, 21.
  • Alam, T., Qamar, S., Dixit, A., & Benaida, M. (2020). Genetic algorithm: Reviews, implementations, and applications. arXiv preprint arXiv:2007.12673. https://doi.org/10.48550/arXiv.2007.12673
  • Alp, G., & Soygazi, F. (2024, May). Meta-Heuristic Supported Feature Selection in Classification Algorithms for Diabetes Diagnosis. In 2024 32nd Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE. 10.1109/SIU61531.2024.10601062
  • Anguita, D., Ghelardoni, L., Ghio, A., Oneto, L., & Ridella, S. (2012, April). The'K'in K-fold Cross Validation. In ESANN (Vol. 102, pp. 441-446).
  • Appiahene, P., Asare, J. W., Donkoh, E. T., Dimauro, G., & Maglietta, R. (2023). Detection of iron deficiency anemia by medical images: a comparative study of machine learning algorithms. BioData mining, 16(1), 2. https://doi.org/10.1186/s13040-023-00319-z
  • Badem, H., Basturk, A., Caliskan, A., & Yuksel, M. E. (2017). A new efficient training strategy for deep neural networks by hybridization of artificial bee colony and limited–memory BFGS optimization algorithms. Neurocomputing, 266, 506-526. https://doi.org/10.1016/j.neucom.2017.05.061
  • Badem, H. (2019). Parkinson Hastaliğinin Ses Sinyalleri Üzerinden Makine Öğrenmesi Teknikleri ile Tanimlanmasi. Niğde Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi, 8(2), 630-637. 10.28948/ngumuh.524658
  • Cheng, L., Nie, Y., Wen, H., Li, Y., Zhao, Y., Zhang, Q., ... & Fu, S. (2024). An ensemble machine learning model for predicting one-year mortality in elderly coronary heart disease patients with anemia. Journal of Big Data, 11(1), 1-20. https://doi.org/10.1186/s40537-024-00966-x
  • Eke, İ. (2022). Optimum PID Kazançları Genetik Algoritma İle Hesaplanan Otomatik Gerilim Regülatörü. International Journal of Engineering Research and Development, 14(3), 351-361. https://doi.org/10.29137/umagd.1176936
  • Gizzi, E., Nair, L., Chernova, S., & Sinapov, J. (2022). Creative problem solving in artificially intelligent agents: A survey and framework. Journal of Artificial Intelligence Research, 75, 857-911. https://doi.org/10.1613/jair.1.13864
  • Gülmez, B. (2023). Market zinciri ürün dağıtımı probleminin farklı genetik algoritma versiyonları ile çözümü ve karşılaştırması. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 180-196. Holland, J. H. (1992). Genetic algorithms. Scientific american, 267(1), 66-73.
  • Inneci, T., & Badem, H. (2023). Detection of Corneal Ulcer Using a Genetic Algorithm-Based Image Selection and Residual Neural Network. Bioengineering, 10(6), 639. https://doi.org/10.3390/bioengineering10060639
  • Kaleli, S. S. (2023). Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma ile Analizi ve Optimum Portföy Oluşturma. MANAS Sosyal Araştırmalar Dergisi, 12(2), 557-565. https://doi.org/10.33206/mjss.1215054
  • Karaboğa, D. (2020). Yapay zeka optimizasyon algoritmaları (7.Baskı). Nobel Akademi Yayıncılık.
  • Katoch, S., Chauhan, S. S., & Kumar, V. (2021). A review on genetic algorithm: past, present, and future. Multimedia tools and applications, 80, 8091-8126. https://doi.org/10.1007/s11042-020-10139-6
  • Keklik, G., & Özcan, B. D. (2023). Genetik Algoritmaların İşleyişi ve Genetik Algoritma Uygulamalarında Kullanılan Operatörler. Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 6(1), 1052-1066. https://doi.org/10.47495/okufbed.1161413
  • Khan, J. R., Chowdhury, S., Islam, H., & Raheem, E. (2019). Machine learning algorithms to predict the childhood anemia in Bangladesh. Journal of Data Science, 17(1), 195-218. 10.6339/JDS.201901_17(1).0009
  • Lambora, A., Gupta, K., & Chopra, K. (2019, February). Genetic algorithm-A literature review. In 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 380-384). IEEE, 10.1109/COMITCon.2019.8862255
  • Mirjalili, S., Song Dong, J., Sadiq, A. S., & Faris, H. (2020). Genetic algorithm: Theory, literature review, and application in image reconstruction. Nature-inspired optimizers: Theories, literature reviews and applications, 69-85. https://doi.org/10.1007/978-3-030-12127-3_5
  • Narayan, Y. (2021). Comparative analysis of SVM and Naive Bayes classifier for the SEMG signal classification. Materials Today: Proceedings, 37, 3241-3245. https://doi.org/10.1016/j.matpr.2020.09.093
  • Öklü, M., & Canbay, P. (2023). Makine Öğrenmesi Yöntemleri ile Şehirlerin Hava Kalitesi Tahmini. International Journal of Advances in Engineering and Pure Sciences, 35(1), 39-53. https://doi.org/10.7240/jeps.1175507
  • Pertiwi, D. A. A., Ahmad, K., Salahudin, S. N., Annegrat, A. M., & Muslim, M. A. (2024). Using genetic algorithm feature selection to optimize XGBoost performance in Australian credit. Journal of Soft Computing Exploration, 5(1), 92-98. https://doi.org/10.52465/joscex.v5i1.302
  • Peterson, L. E. (2009). K-nearest neighbor. Scholarpedia, 4(2), 1883. 10.4249/scholarpedia.1883
  • Saputra, D. C. E., Sunat, K., & Ratnaningsih, T. (2023, February). A new artificial intelligence approach using extreme learning machine as the potentially effective model to predict and analyze the diagnosis of anemia. In Healthcare (Vol. 11, No. 5, p. 697). MDPI. https://doi.org/10.3390/healthcare11050697
  • Seymen, V. (2014). Demir eksikliği anemisi hastalığının tespitinde kullanılan sınıflandırma algoritmalarının karşılaştırılması (Master's thesis, Sakarya Universitesi (Turkey)).
  • Shukla, A., Pandey, H. M., & Mehrotra, D. (2015, February). Comparative review of selection techniques in genetic algorithm. In 2015 international conference on futuristic trends on computational analysis and knowledge management (ABLAZE) (pp. 515-519). IEEE. 10.1109/ABLAZE.2015.7154916
  • Taşcı, E., & Onan, A. (2016). K-en yakın komşu algoritması parametrelerinin sınıflandırma performansı üzerine etkisinin incelenmesi. Akademik Bilişim, 1(1), 4-18.
  • Türkkahraman, Ş. M., & Karabulut, K. (2023). Sosyal Ağ Varlığında Takım Oluşturma Problemine Hibrit Bir Genetik Algoritma Önerisi. Dokuz Eylül Üniversitesi Mühendislik Fakültesi Fen ve Mühendislik Dergisi, 25(73), 181-192. https://doi.org/10.21205/deufmd.2023257315
  • Vohra, R., Dudyala, A. K., Pahareeya, J., & Hussain, A. (2022). Decision rules generation using decision tree classifier and their optimization for anemia classification. In Inventive Computation and Information Technologies: Proceedings of ICICIT 2021 (pp. 721-737). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-16-6723-7_53
  • Wan, H. (2024). Applying the genetic algorithm to optimization problems. WIT Transactions on Information and Communication Technologies, 2. 10.2495/AIENG930332
  • Weile, D. S., & Michielssen, E. (1997). Genetic algorithm optimization applied to electromagnetics: A review. IEEE Transactions on Antennas and Propagation, 45(3), 343-353. 10.1109/8.558650
  • World Health Organization (WHO) (2008). Worldwide prevalence of anaemia 1993-2005: WHO global database on anaemia.
  • World Health Organization (WHO). (2014). Global nutrition targets 2025: Stunting policy brief (No. WHO/NMH/NHD/14.3).
  • World Health Organization (WHO). (2024). Guideline on haemoglobin cutoffs to define anaemia in individuals and populations. World Health Organization.
  • Yağmur, N., Temurtaş, H., & Dağ, İ. (2023). Anemi Hastaliğinin Yapay Sinir Ağlari Yöntemleri Kullanilarak Siniflandirilmasi. Journal of Scientific Reports-B, (008), 20-34.
  • Yıldız, T. K., Yurtay, N., & Öneç, B. (2021). Classifying anemia types using artificial learning methods. Engineering Science and Technology, an International Journal, 24(1), 50-70. https://doi.org/10.1016/j.jestch.2020.12.003
  • Zhang, S., & Li, J. (2021). KNN classification with one-step computation. IEEE Transactions on Knowledge and Data Engineering, 35(3), 2711-2723. 10.1109/TKDE.2021.3119140
There are 39 citations in total.

Details

Primary Language Turkish
Subjects Reinforcement Learning, Evolutionary Computation
Journal Section Computer Engineering
Authors

Mehtap Öklü 0000-0002-8833-2231

Hasan Badem 0000-0002-4262-8774

Publication Date March 3, 2025
Submission Date October 4, 2024
Acceptance Date December 12, 2024
Published in Issue Year 2025Volume: 28 Issue: 1

Cite

APA Öklü, M., & Badem, H. (2025). GENETİK ALGORİTMA TEMELLİ ÖZNİTELİK SEÇİMİNİN ANEMİ HASTALIĞININ TESPİTİNE ETKİSİ. Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi, 28(1), 309-321.
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