Öz
Bilgisayarların makine öğrenimi tekniği ile eğitilmesi ile hastaların gereksiz yere zor tetkiklere maruz kalması engellenebilir. Son yıllarda makine öğrenimi tabanlı hastalık değerlendirme yaklaşımı, klinik yöntemlere sağladığı faydalar açısından önem kazanmıştır. Bu yönde yapılan çalışmalarda dikkat çekici bir artış vardır. Bazı kanser türlerini öngörmede sınırlı sayıda klinik yol gösterici parametre vardır ve bu kısıtlılık tedavi gören hastaları oldukça yıpratıcı bir sürece itmektedir. Bu nedenle, geleneksel tıbbın alışılagelmiş prosedürlerinden farklı olarak, herhangi bir kanser türünü tahmin etmede alternatif bir yaklaşım, son yıllarda üzerinde çok çalışılan bir yöntem haline gelen bilgisayar tabanlı değerlendirme yapmaktır. Bu çalışmada, dünya çapında erkeklerde ikinci en yaygın kansere bağlı ölüm olan prostat kanserini değerlendirmek için bir makine öğrenimi (ML) yaklaşımı kullanılacaktır. Bu amaçla bir boyut küçültme tekniği olan öznitelik seçimi ile ML için K-En Yakın Komşu (kNN) algoritması kullanılacaktır. Değerlendirme için açık kaynaklı bir veri tabanı olan Kaggle kullanılmıştır. Kullanılan algoritmanın doğruluk değeri %88 olarak elde edildi.
Anahtar Kelimeler
Kaynakça
- Anand L., et al. (2023). Diagnosis of Prostate Cancer Using GLCM Enabled KNN Technique by Analyzing MRI Images, BioMed Research International, Article ID 3913351.
- Araujo W. B. D., et al. (2023). Method to aid the diagnosis of PCa using machine learning and clinical data, PREPRINT (Version 1), Research Square.
- Bektaş J. et al. (2022). Integrating a novel SRCRN network for segmentation with representative batch-mode experiments for detecting melanoma. Biomedical Signal Processing and Control 71(B): 103218.
- Coudert O. R. et al. (2012). Machine learning for improved pathological staging of prostate cancer: A performance comparison on a range of classifiers, Artificial Intelligence in Medicine, Volume 55, Issue 1, 25-35.
- Couture H. D. et al. (2018). Image analysis with deep learning to predict breast cancer grade, er status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer 4(1):30
- Elkhani N. and Muniyandi R.C. (2017). Intell. Data Anal. 21, S137. Erdem E., and Bozkurt F. (2021). A Comparison of Various Supervised Machine Learning Techniques Prostate Cancer Prediction, Eur. J. Sci. Tech., 21, 610-620.
- Goldenberg, S. L., Nir, G., & Salcudean, S. E. (2019). A new era: artificial intelligence and machine learning in prostate cancer. Nature Reviews Urology, 16(7), 391-403.
- Kulkarni A., Chong D. and Batarseh F. A. (2020). 5 Foundations of data imbalance and solutions for a data democracy, Editor(s): Feras A. Batarseh, Ruixin Yang, Data Democracy, Academic Press, 83-106.
- Mydlo J. H. And Godec C. J. (2016). Prostate Cancer, Academic Press.
- Pellicer-Valero, O.J. et al.(2022). Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of PCa in multiparametric magnetic resonance images. Sci Rep 12, 2975.
- Qaiser T, Rajpoot N. M. (2019). Learning where to see: a novel attention model for automated immunohistochemical scoring. IEEE Trans Med Imag 38(11):2620–2631.
- Regnier-Coudert O., et al. (2012). Machine learning for improved pathological staging of prostate cancer: A performance comparison on a range of classifiers, Artificial Intelligence in Medicine, 55(1), 25–35.
- Sajid S. (2018). Prostate cancer dataset, [Online]. Available: https://www.kaggle.com/sajidsaifi/prostate-cancer
- Srivenkatesh, M. (2020). Prediction of PCa using Machine Learning Algoritmhs International Journal of Recent Technology and Engineering. 8 (5).
- Street W.N., Wolberg W.H. and Mangasarian O.L (1993) Nuclear feature extraction for breast tumor diagnosis. International Symposium Electronic Imaging: Science and Technology; 1-4; San Jose, CA, USA. vol. 1905, pp. 861-870.
- Torgo L., et al. (2013). SMOTE for Regression, Progress in Artificial Intelligence, Lec-ture Notes in Computer Science, vol 8154, Springer, Berlin, Heidelberg.
- Valero P. et al. (2022). Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images. Scientific reports, 12(1), 2975.
- Wang K. et al. (2022). Machine learning prediction of PCa from transrectal ultrasound video clips. Front. Oncol. 12:948662.
- Wang C. et al. (2022). PCa Risk Prediction and Online Calculation Based on Machine Learning Algorithm, Chin Med Sci J. Sep 30;37(3):210-217.
- Yoo, S. et al. PCa Detection using Deep Convolutional Neural Networks. Sci Rep 9, 19518
- Zhang L. et al. (2021). A new approach to diagnosing PCa through magnetic resonance imaging, Alexandria Engineering Journal, Volume 60, Issue 1, 897-904.
Öz
By training computers with machine learning technique, patients can be prevented from being exposed to unnecessarily difficult examinations. In recent years, machine learning-based disease assessment approach has gained importance in terms of the benefits it provides to clinical methods. There is a remarkable increase in studies in this direction. There are a limited number of clinical guiding parameters in predicting some types of cancer, and this limitation pushes the patients under treatment to a very frustrating process. For this reason, apart from ordinary procedure of the traditional medicine, an alternative approach to predict the any type of cancer is making a computer-based evaluation that has become a highly studied method in recent years. In this study, a machine learning (ML) approach will be used to evaluate prostate cancer, which is the second most common cancer-related death in men worldwide. For this purpose, the K-Nearest Neighbor (kNN) algorithm based on ML will be used with feature selection, which is a dimension reduction technique. An open source database, Kaggle, was used for the evaluation. The accuracy value of the used algorithm was found 88%.
Anahtar Kelimeler
Etik Beyan
The author declares that this study complies with research and publication ethics.
Destekleyen Kurum
Non.
Teşekkür
N/A
Kaynakça
- Anand L., et al. (2023). Diagnosis of Prostate Cancer Using GLCM Enabled KNN Technique by Analyzing MRI Images, BioMed Research International, Article ID 3913351.
- Araujo W. B. D., et al. (2023). Method to aid the diagnosis of PCa using machine learning and clinical data, PREPRINT (Version 1), Research Square.
- Bektaş J. et al. (2022). Integrating a novel SRCRN network for segmentation with representative batch-mode experiments for detecting melanoma. Biomedical Signal Processing and Control 71(B): 103218.
- Coudert O. R. et al. (2012). Machine learning for improved pathological staging of prostate cancer: A performance comparison on a range of classifiers, Artificial Intelligence in Medicine, Volume 55, Issue 1, 25-35.
- Couture H. D. et al. (2018). Image analysis with deep learning to predict breast cancer grade, er status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer 4(1):30
- Elkhani N. and Muniyandi R.C. (2017). Intell. Data Anal. 21, S137. Erdem E., and Bozkurt F. (2021). A Comparison of Various Supervised Machine Learning Techniques Prostate Cancer Prediction, Eur. J. Sci. Tech., 21, 610-620.
- Goldenberg, S. L., Nir, G., & Salcudean, S. E. (2019). A new era: artificial intelligence and machine learning in prostate cancer. Nature Reviews Urology, 16(7), 391-403.
- Kulkarni A., Chong D. and Batarseh F. A. (2020). 5 Foundations of data imbalance and solutions for a data democracy, Editor(s): Feras A. Batarseh, Ruixin Yang, Data Democracy, Academic Press, 83-106.
- Mydlo J. H. And Godec C. J. (2016). Prostate Cancer, Academic Press.
- Pellicer-Valero, O.J. et al.(2022). Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of PCa in multiparametric magnetic resonance images. Sci Rep 12, 2975.
- Qaiser T, Rajpoot N. M. (2019). Learning where to see: a novel attention model for automated immunohistochemical scoring. IEEE Trans Med Imag 38(11):2620–2631.
- Regnier-Coudert O., et al. (2012). Machine learning for improved pathological staging of prostate cancer: A performance comparison on a range of classifiers, Artificial Intelligence in Medicine, 55(1), 25–35.
- Sajid S. (2018). Prostate cancer dataset, [Online]. Available: https://www.kaggle.com/sajidsaifi/prostate-cancer
- Srivenkatesh, M. (2020). Prediction of PCa using Machine Learning Algoritmhs International Journal of Recent Technology and Engineering. 8 (5).
- Street W.N., Wolberg W.H. and Mangasarian O.L (1993) Nuclear feature extraction for breast tumor diagnosis. International Symposium Electronic Imaging: Science and Technology; 1-4; San Jose, CA, USA. vol. 1905, pp. 861-870.
- Torgo L., et al. (2013). SMOTE for Regression, Progress in Artificial Intelligence, Lec-ture Notes in Computer Science, vol 8154, Springer, Berlin, Heidelberg.
- Valero P. et al. (2022). Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images. Scientific reports, 12(1), 2975.
- Wang K. et al. (2022). Machine learning prediction of PCa from transrectal ultrasound video clips. Front. Oncol. 12:948662.
- Wang C. et al. (2022). PCa Risk Prediction and Online Calculation Based on Machine Learning Algorithm, Chin Med Sci J. Sep 30;37(3):210-217.
- Yoo, S. et al. PCa Detection using Deep Convolutional Neural Networks. Sci Rep 9, 19518
- Zhang L. et al. (2021). A new approach to diagnosing PCa through magnetic resonance imaging, Alexandria Engineering Journal, Volume 60, Issue 1, 897-904.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Bilgisayar Görüşü |
| Bölüm | Makaleler |
| Yazarlar | |
| Erken Görünüm Tarihi | 29 Aralık 2023 |
| Yayımlanma Tarihi | 31 Aralık 2023 |
| Gönderilme Tarihi | 29 Ekim 2023 |
| Kabul Tarihi | 1 Aralık 2023 |
| Yayımlandığı Sayı | Yıl 2023 Cilt: 9 Sayı: 2 |
