Prostat Kanseri Tanısında Harris Hawks Optimizasyon (HHO) Algoritması ve Kuantum Öğrenmeye Dayalı Hibrit Bir Model Geliştirilmesi

Year 2025, Volume: 28 Issue: 3, 1063 - 1072, 13.06.2025

Melisa Rahebi

https://doi.org/10.2339/politeknik.1611704

Abstract

Prostat kanseri (PK) önemli bir sağlık sorunudur ve kısmen kullanılan tanı tekniklerinin dezavantajları nedeniyle erkeklerde başlıca ölüm nedenleri arasında yer almaktadır. Her yıl, bu mevcut teşhis teknikleri, sadece zamanında doğru bir teşhis alamadıkları için birçok erkeğin hayatını kaybetmesine neden olmaktadır. PK teşhisi için potansiyel olarak pratik ve uygun maliyetli bir yaklaşım, yapay zeka, özellikle de makine öğrenimi uygulamaktır. Bu çalışma, %38'i hasta ve %62'si sağlıklı olan 100 erkeğin klinik verileri üzerinde prostat kanseri teşhisi için bir makine öğrenimi (ML) modeli geliştirmeyi amaçlamaktadır. Önerilen model Harris Hawk Optimizasyonu (HHO) ve Kuantum Öğrenme (QL) yöntemlerini birleştirmektedir. Sonuçlar, bu yeni yaklaşımın diğer ML yaklaşımlarına göre %97,84 oranında daha iyi doğruluk sağladığını ortaya koymaktadır.

Keywords

Prostat kanseri tespiti, Makine Öğrenimi, Kanser tespiti, Kuantum öğrenme, Harris Hawk Optimizasyonu (HHO)

Development A Hybrid Model Based on Harris Hawks Optimization (HHO) Algorithm and Quantum Learning to Diagnosis of Prostate Cancer

Abstract

Prostate cancer (PC) represents a significant health problem and stands among the primary mortality causes in men, partly due to the drawbacks of the diagnostic techniques currently used. Annually, these current diagnosis techniques cause many men to lose their lives simply because they cannot get an accurate diagnosis on time. A potentially practical and cost-effective approach for diagnosing PC is applying artificial intelligence, particularly machine learning. This work is aimed at developing a machine learning (ML) model for the diagnosis of prostate cancer on clinical data of 100 men, among whom 38% were suffering and 62% were healthy. The proposed model combines the Harris Hawk Optimization (HHO) and Quantum Learning (QL) methods. The results reveal that this new approach provides better accuracy, 97.84%, than other ML approaches.

Keywords

Prostate cancer detection, Machine Learning, Cancer detection, Quantum learning, Harris Hawk Optimization (HHO)

References

• [1] S. C. Darby, P. McGale, C. W. Taylor, and R. Peto, “Long-term mortality from heart disease and lung cancer after radiotherapy for early breast cancer: prospective cohort study of about 300 000 women in US SEER cancer registries,” Lancet Oncol., 6(8):557–565, (2005).
• [2] C. Mattiuzzi and G. Lippi, “Current cancer epidemiology,” Journal of Epidemiology and Global Health, 9(4):217–222, (2019).
• [3] [3] S. M. Alzahrani, H. A. Al Doghaither, and A. B. Al-Ghafari, “General insight into cancer: An overview of colorectal cancer,” Molecular and Clinical Oncology., 15(6):271, (2021).
• [4] A. M. Agre, A. C. Upade, M. A. Yadav, and S. B. Kumbhar, “A Review on Breasr Cancer and Its Management,” World Journal of Pharmaceutical Research, 10(2):408–437, (2021).
• [5] A. P. Mamede et al., “A new look into cancer—a review on the contribution of vibrational spectroscopy on early diagnosis and surgery guidance,” Cancers (Basel)., 13(21):5336, (2021).
• [6] T. Saba, “Recent advancement in cancer detection using machine learning: Systematic survey of decades, comparisons and challenges,” Journal of Infection and Public Health 13(9):1274–1289, (2020).
• [7] S. Warnakulasuriya et al., “Oral potentially malignant disorders: A consensus report from an international seminar on nomenclature and classification, convened by the WHO Collaborating Centre for Oral Cancer,” Journal of Oral Diseases 27(8):1862–1880, (2021).
• [8] M. Sharma, S. Gupta, B. Dhole, and A. Kumar, “The prostate gland,” Basics Hum. Androl. A Textb.17–35, (2017).
• [9] P. Porzycki and E. Ciszkowicz, “Modern biomarkers in prostate cancer diagnosis,” Central European Journal of Urology., 73(3):300, (2020).
• [10] K. M. Chan, J. M. Gleadle, M. O’Callaghan, K. Vasilev, and M. MacGregor, “Prostate cancer detection: A systematic review of urinary biosensors,” Prostate Cancer Prostatic Dis., 25(1):39–46, (2022).
• [11] G. Gandaglia et al., “Epidemiology and prevention of prostate cancer,” European Urology Oncology., 4(6):877–892, (2021).
• [12] M. Oczkowski, K. Dziendzikowska, A. Pasternak-Winiarska, D. Włodarek, and J. Gromadzka-Ostrowska, “Dietary factors and prostate cancer development, progression, and reduction,” Nutrients, 13(2):496, (2021).
• [13] N. N. Junejo and S. S. AlKhateeb, “BRCA2 gene mutation and prostate cancer risk: Comprehensive review and update,” Saudi Medical Journal., 41(1):9, (2020).
• [14] N. Hinata and M. Fujisawa, “Racial differences in prostate cancer characteristics and cancer-specific mortality: an overview,” World J. Mens. Health, 40(2):217, (2022).
• [15] A. Barsouk et al., “Epidemiology, staging and management of prostate cancer,” International Journal of Medical Sciences., 8(3):28, (2020).
• [16] B. A. Akinnuwesi et al., “Application of support vector machine algorithm for early differential diagnosis of prostate cancer,” Data Science and Management., 6(1):1–12, (2023).
• [17] I. S. C. Williams et al., “Modern paradigms for prostate cancer detection and management,” The Medical Journal of Australia. 424–433, (2022).
• [18] N. H. and M. R. Council, “Prostate-specific antigen (PSA) testing in asymptomatic men: Evidence Evaluation Report,” (2013).
• [19] C. He et al., “Accurate tumor subtype detection with raman spectroscopy via variational autoencoder and machine learning,” ACS omega, 7(12):10458–10468, (2022).
• [20] S. Folland, A. C. Goodman, M. Stano, and S. Danagoulian, The economics of health and health care. Routledge, (2024).
• [21] M. Paz-Zulueta):Parás-Bravo, D. Cantarero-Prieto, C. Blázquez-Fernández, and A. Oterino-Durán, “A literature review of cost-of-illness studies on the economic burden of multiple sclerosis.,” Multiple Sclerosis and Related Disorders, (43):102162, (2020).
• [22] Y. Vodovotz et al., “Prioritized research for the prevention, treatment, and reversal of chronic disease: recommendations from the lifestyle medicine research summit,” Frontiers in Medicine., (7):585744, (2020).
• [23] J. Yang, R. Xu, C. Wang, J. Qiu, B. Ren, and L. You, “Early screening and diagnosis strategies of pancreatic cancer: a comprehensive review,” Cancer Commun., 41(12):1257–1274, (2021).
• [24] E. Yaghoubi, E. Yaghoubi, A. Khamees, D. Razmi, and T. Lu, “A systematic review and meta-analysis of machine learning, deep learning, and ensemble learning approaches in predicting EV charging behavior,” Engineering Applications of Artificial Intelligence., (135):108789, (2024).
• [25] E. Yaghoubi, E. Yaghoubi, A. Khamees, and A. H. Vakili, “A systematic review and meta-analysis of artificial neural network, machine learning, deep learning, and ensemble learning approaches in field of geotechnical engineering,” Neural Computing and Applications. 1–45, (2024).
• [26] S. Quazi, “Artificial intelligence and machine learning in precision and genomic medicine,” Medical Oncology., 39(8):120, (2022).
• [27] D. J. Van Booven et al., “A systematic review of artificial intelligence in prostate cancer,” Research and Reports in Urology.31–39, (2021).
• [28] A. Baydoun et al., “Artificial intelligence applications in prostate cancer,” Prostate Cancer Prostatic Dis., 27(1):37–45, (2024).
• [29] D. Peral-García, J. Cruz-Benito, and F. J. García-Peñalvo, “Systematic literature review: Quantum machine learning and its applications,” Computational Science Journal., (51):100619, (2024).
• [30] P. Saha, “Quantum Machine Learning with Application to Progressive Supranuclear Palsy Network Classification,” arXiv Prepr. arXiv2407.06226, (2024).
• [31] S. Khurana, “Quantum Machine Learning: Unraveling a New Paradigm in Computational Intelligence,” Quantum, (74):1, (2024).
• [32] S. Hussain, X. Songhua, M. Aslam, M. Waqas, and S. Hussain, “Quantum Deep Learning for Automatic Chronic Kidney Disease Identification and Classification with CT images,” Springer Science and Business Media LLC, (2024).
• [33] W. El Maouaki, T. Said, and M. Bennai, “Quantum Support Vector Machine for Prostate Cancer Detection: A Performance Analysis,” arXiv Prepr. arXiv2403.07856, (2024).
• [34] S. Toledo-Cortés, D. H. Useche, H. Müller, and F. A. González, “Grading diabetic retinopathy and prostate cancer diagnostic images with deep quantum ordinal regression,” Computers in Biology and Medicine., (145):105472, (2022).
• [35] M. Ghosh, S. Sen, R. Sarkar, and U. Maulik, “Quantum squirrel inspired algorithm for gene selection in methylation and expression data of prostate cancer,” Applied Soft Computing Journal., (105):107221, (2021).
• [36] M. Gerlinger et al., “Intratumor heterogeneity and branched evolution revealed by multiregion sequencing,” The New England Journal of Medicine., 366(10):883–892, (2012).
• [37] S. T. Tagawa et al., “Survival outcomes in patients with chemotherapy-naive metastatic castration-resistant prostate cancer treated with enzalutamide or abiraterone acetate,” Prostate Cancer Prostatic Dis., 24(4):1032–1040, (2021).
• [38] Z. Kote-Jarai et al., “Seven prostate cancer susceptibility loci identified by a multi-stage genome-wide association study,” Nature Genetics., 43(8):785–791, (2011).
• [39] J. Zhang et al., “Prostatic adenocarcinoma presenting with metastases to the testis and epididymis: A case report,” Oncology Letters., 11(1):792–794, (2016).
• [40] L. Hussain et al., “Prostate cancer detection using machine learning techniques by employing combination of features extracting strategies,” Cancer Biomarkers, 21(2):393–413, (2018).
• [41] M. Schuld, I. Sinayskiy, and F. Petruccione, “An introduction to quantum machine learning,” Contemporary Physics., 56(2):172–185, (2015).
• [42] J. M. Castillo T, M. Arif, W. J. Niessen, I. G. Schoots, and J. F. Veenland, “Automated classification of significant prostate cancer on MRI: a systematic review on the performance of machine learning applications,” Cancers (Basel)., 12(6):1606, (2020).
• [43] M. Hosseinzadeh, A. Saha):Brand, I. Slootweg, M. de Rooij, and H. Huisman, “Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge,” European Radiology.):1–11, (2022).
• [44] J. S. Bosma, A. Saha, M. Hosseinzadeh, I. Slootweg, M. de Rooij, and H. Huisman, “Semisupervised learning with report-guided pseudo labels for deep learning–based prostate cancer detection using biparametric MRI,” Radiology Artificial Intelligence., 5(5):e230031, (2023).
• [45] E. Yang, K. Shankar, S. Kumar, C. Seo, and I. Moon, “Equilibrium optimization algorithm with deep learning enabled prostate cancer detection on MRI images,” Biomedicines, 11(12):3200, (2023).
• [46] S. K. Singh et al., “A novel deep learning-based technique for detecting prostate cancer in MRI images,” Multimedia Tools and Applications., 83(5):14173–14187, (2024).
• [47] P. K.-F. Chiu et al., “Enhancement of prostate cancer diagnosis by machine learning techniques: an algorithm development and validation study,” Prostate Cancer and Prostatic Diseases., 25(4):672–676, (2022).
• [48] M. S. Alzboon and M. S. Al-Batah, “Prostate Cancer Detection and Analysis using Advanced Machine Learning,” International Journal of Engineering and Computer Science., 14(8), (2023).
• [49] A. M. Alshareef et al., “Optimal deep learning enabled prostate cancer detection using microarray gene expression,” Journal of Healthcare Engineering., 2022(1):7364704, (2022).
• [50] A. A. Heidari, S. Mirjalili, H. Faris, I. Aljarah, M. Mafarja, and H. Chen, “Harris hawks optimization: Algorithm and applications,” Future Generation Computer Systems Journal., (97):849–872, (2019).