Prediction of post-treatment survival expectancy in head & neck cancers by machine learning methods

Yıl 2020, Cilt: 5 Sayı: 1, 5 - 9, 30.06.2020

Hıdır Selçuk

Öz

In this study, survival for head and neck cancer disease was estimated using machine learning methods. Starting from the date on which the head and neck cancer disease was diagnosed, without a maximum time limit, at the end of the minimum 8 month period, it is estimated that the patient will be alive or not. Seven classifying machine-learning predictive methods were used in the study. The main goal of this study is to estimate the survivability of head and neck cancer patients and to provide a decision aid for cancer management with applied estimation methods and results. The results obtained by the application of the designed methods are examined and results with extremely high accuracy rates are obtained.

Anahtar Kelimeler

Machine learning, classification, artificial neural network, support vector machines, decision tree, logistic regression, linear disriminant, nearest neighbor ., artificial neural network, Support vector machines

Kaynakça

• Ahmed, F. E. (2005). Artificial neural networks for diagnosis and survival prediction in colon cancer, Molecular Cancer, 29, doi:10.1186/1476-4598-4-29.
• Allison, D. B., Maleki, Z. (2016). HPV-related head and neck squamous cell carcinoma: Anupdate and review, Journal of the American Society of Cytopathology, 5, 203-215.
• Asria, H., Mousannif, H., Al Moatassime, H., Noel, T. (2016). Using Machine Learning Algorithms for Breast Cancer Risk, Prediction and Diagnosis, Procedia Computer Science, 83, 1064 – 1069.
• Breiman, L., Freidman, J. H., Olshen R. A., and Stone, C. J. (1984). Classification and Regression Trees, Chapman and Hall, New York, USA. Cancer Imaging Archieve (2018). http://www.cancerimagingarchive.net/ , last accessed date: February 10, 2018.
• Cai, J., and Huang, X. (2018). Modified Sparse Linear-Discriminant Analysis via, Nonconvex Penalties, IEEE Transactions on Neural Networks and Learning Systems, Early Acces, 1-10.
• Chi, C. L., Street, W. N., Wolberg, W. H. (2007). Application of Artificial Neural Network- Based Survival Analysis on Two Breast Cancer Datasets, AMIA Annu Symp Proc., 130–134.
• Chien, C. F., Chen, L. F. (2008). Data Mining to Improve Personnel Selection and Enhance Human Capital: A Case Study in High-Technology Industry, Expert Systems with Applications, 34, 280 - 290 . Cortes, C., Vapnik, V. (1995). Support-Vector Network, Machine Learning, 20, 273–297.
• Cover, T. M., and Hart, P. E. (1967). Nearest neighbor pattern classification, IEEE Transactions on Information Theory, 13, 21–27.
• De Laurentiis, M., and Ravdin, P. M. (1994). Survival analysis of censored data: Neural network analysis, detection of complex interactions between variables, Breast Cancer Research and Treatment, 32, 113-118.
• Devi, M. A., Ravi, S., Vaishnavi, J., and Punitha, S. (2016). Classification of Cervical Cancer using Artificial Neural Networks, Procedia Computer Science, 89, 465-472.
• Discriminant analysis (2018). https://www.mathworks.com/help/stats/discriminantanalysis.html, last accessed date: February, 14, 2018.
• Dolgobrodov, S. G., Moore, P., Marshall, R., Bittern, R., Steele, R. J. C., Cuschieri, A. (2007). Artificial Neural Network: Predicted vs. Observed Survival in Patients with Colonic Cancer, Diseases of the Colon & Rectum, 50, 184–191.
• Drago, G. P., Setti, E., Licitra, L., and Liberati, D. (2002). Forecasting the Performance Status of Head and Neck Cancer Patient Treatment by an Interval Arithmetic Pruned -Perceptron, IEEE Transactions on Biomedical Engineering, 49, 782-787.
• Duca, A., Bacciu, C., Marchetti, A. (2017). A K-Nearest Neighbor Classifier for Ship Route Prediction, IEEE OCEANS - Aberdeen, 1 – 6. Elkhalil, K., Kammoun, A., Couillet, R., Al-Naffouri, T. Y., and Alouini, M. S. (2017). Asyptotic Performance of Regularized Quadratic Discriminant Analysis Based Classifiers, 2017 IEEE International Wokrshop on Machine Learning for Signal Processing Tokyo, 25–28.
• Fisher, R. A. (1936). The Use of Multiple Measurements in Taxonomic Problems, Annals of Eugenics, 7, 179–188.
• Francis, N. K., Luther, A., Salib, E., Allanby, L., Messenger, D., Allison, A. S., Smart, N. J., Ockrim, J. B. (2015). The use of artificial neural networks to predict delayed discharge and readmission in enhanced recovery following laparoscopic, colorectal cancer surgery, Tech Coloproctol, 19, 419 – 428.
• Galbiatti, A. L. S., Junior, J. A. P., Maníglia, J. V., Rodrigues, C. D. S., Pavarino, É. C., Bertollo, E. M. G. (2013). Head and neck cancer: causes, prevention and treatment, Braz J Otorhinolaryngol, 79, 239-47.
• Gao, L., Ye, M., Lu, X., Huang, D. (2017). Hybrid Method Based on Information Gain and Support Vector Machine for Gene Selection in Cancer Classification, Genomics, Proteomics & Bioinformatics, 15, 389-395.
• Geng, P., Sakhanenko, L. (2016). Parameter estimation for the logistic regression model under case-control study, Statistics and Probability Letters, 109, 168–177.
• Ghaddar, B., Sawaya, J. N. (2018). High dimensional data classification and feature selection using support vector machines, European Journal of Operational Research, 265, 993-1004.
• Ilias, S., Tahir, N. M., Jailani, R. (2016). Feature extraction of autism gait data using principal component analysis and linear discriminant analysis, IEEE Industrial Electronics and Applications Conference IEACon., 275 – 279.
• Iraji, M. S. (2017). Prediction of post-operative survival expectancy in thoracic lung cancer surgery with soft computing, Journal of Applied Biomedicine, 15, 151–159.
• Jemal, A., Bray, F., Center, M. M., Ferlay, J., Ward, E., Forman, D. (2011). Global cancer statistics, CA Cancer J Clin, 61, 69-90.
• Jones, A. S., Taktak, A. G. F., Helliwell, T. R., Fenton, J. E., Birchall, M. A., Husband, D. J., Fisher A. C. (2006). An artificial neural network improves prediction of observed survival in patients with laryngeal squamous carcinoma, Eur Arch Otorhinolaryngol, 263, 541–547.
• Kavzaoglu, T., Colkesen, I. (2010). Investigation of the Effects of Kernel Functions in Satellite Image Classification Using Support Vector Machines, Map Journal July, 144, 73-82.
• Lachenbruch, P. A. (1975). Discriminant analysis, Hafner Press, New York, USA.
• Lachenbruch, P. A., and Mickey, M. R. (1968). Estimation of error rates in discriminant analysis, Technometrics 10, 1-11.
• Lawi, A., La Wungo, S., Manjang, S. (2017). Identifying Irregularity Electricity Usage of Customer Behaviors using Logistic Regression and Linear Discriminant Analysis, IEEE 3rd International Conference on Science in Information Technology (ICSITech), 552-557.
• Lee, Y., Madayambath, S. C., Liu, Y., Da-Ting, L., Chen, R. and Bhattacharyya, S., S. (2017). Online Learning in Neural Decoding Using Incremental Linear Discriminant Analysis, IEEE2017 IEEE International Conference on Cyborg and Bionic Systems Beijing China, 173-177.
• Li, W., Du, Q., Zhang, F., Hu, W. (2014). Collaborative Representation Based K-Nearest Neighbor Classifier for Hyperspectral Imagery , Hyperspectral Image and Signal Processing: Evolution in Remote Sensing , WHISPERS DOI: 10.1109/WHISPERS.2014.8077601.
• Lyncha, C. M., Abdollahib, B., Fuquac, J. D., de Carloc, A. R., Bartholomaic, J. A., Balgemannc, R. N., van Berkeld, V. H., Frieboesc, H. B. (2017). Prediction of lung cancer patient survival via supervised machine learning classification techniques, International Journal of Medical Informatics, 108, 1–8.
• Machine learning methods, (2018). https://www.mathworks.com , last accessed date: February 12, 2018.
• McGurk, M., Goodger, N. M. (2000). Head and neck cancer and its treatment: historical review, British Journal of Oral and Maxillofacial Surgery, 38, 209–220.
• Madadum, H., Becerikli, Y. (2017). The implementation of Support Vector Machine (SVM) using FPGA for human detection, 10th International Conference on Electrical and Electronics Engineering ELECO, 1286 – 1290.
• Maund, I., Jefferies, S. (2015). Squamous cell carcinoma of the oral cavity, oropharynx and upper oesophagus, Medicine, 43, 197-201.
• Nefedow, A., Ye, J. Ye, Kulikowski, C., Muchnik, I., Morgan, K. (2009). Comparative Analysis of Support Vector Machines Based on Linear and Quadratic Optimization Criteria, IEEE International Conference on Machine Learning and Applications 288 – 293.
• Ochi, T., Murase, K., Fujii, T., Kawamura, M., Ikezoe, J. (2002). Survival prediction using artificial neural networks in patients with uterine, cervical cancer treated by radiation therapy alone, Int J Clin Oncol 7, 294–300.
• Osuna, E. E., Freund, R., Girosi, F. (1997). Support Vector Machines: Training and Applications, Massachusetts Institute of Technology and Artificial Intelligence Laboratory 144, Massachusetts.
• Ravdin, P, M., and Clark, G. M. (1992). A practical application of neural network analysis for predicting outcome of individual breast cancer patients, Breast Cancer Research and Treatment, 22, 285-293.
• Razanamahandry, L. C., Andrianisa, H. A., Karoui, H., Podgorski, J., Yacouba, H. (2018). Prediction model for cyanide soil pollution in artisanal gold mining area by using logistic regression, Catena, 162, 40–50.
• Ripley, R. M., Harris A. L., and Tarassenko, L. (1998). Neural Network Models for Breast Cancer Prognosis, Neural Comput & Applic, 7, 367 375.
• Shukla, N., Hagenbuchner, M., Wi, K. T., Yang, J. (2018). Breast cancer data analysis for survivability studies and prediction, Computer Methods and Programs in Biomedicine, 155, 199–208.
• Shukla, R. S., Aggarwal, Y. (2017). Nonlinear Heart Rate Variability based artificial intelligence in lung cancer prediction, Journal of Applied Biomedicine xxx xxx–xxx.
• Stephen, E. F., Hsieh, Y., Rivadinera, A., Beer, T. M., Mori, M., Garzotto, M. (2006). Classification and Regression Tree Analysis for the Prediction of Aggressive Prostate Cancer on Biopsy, The Journal of Urology, 175, 918-922.
• Support Vector Machines (2018). https://www.mathworks.com/help/stats/support-vector- machines-for-binary classification.html , last accessed date February 14, 2018.
• Tsangaratos, P., Ilia, I. (2016). Comparison of a logistic regression and Naïve Bayes classifier in landslide susceptibility assessments: The influence of models complexity and training dataset size, Catena, 145, 164–179.
• Walczak, S., Velanovich, V. (2017). Improving prognosis and reducing decision regret for pancreatic cancer treatment using artificial neural networks, Decision Support Systems xxx xxx–xxx.
• Walczak, S., Velanovich, V. (2017). An Evaluation of Artificial Neural Networks in Predicting Pancreatic Cancer Survival, J Gastrointest Surg., 21, 1606–1612.
• Wang, H., Zheng, B., Yoon, W., Ko, H. S. (2018). A support vector machine-based ensemble algorithm for breast cancer diagnosis, European Journal of Operational Research, 267, 687-699.
• Whsu, C., Lin, C. J. (2002). A Comparison of Methods for Multiclass Support Vector Machines, IEEE Transactions On Neural Networks, 13, 415-425.
• Wróbel, L., Gudy´s A., and Sikora, M. (2017). Learning rule sets from survival data, BMC, Bioinformatics 285 DOI 10.1186/s12859-017-1693-.
• Wu, C., Wu, Y., Liang, P., Wu, C., Peng, S. F., Chiu, H. W. (2017). Disease-free survival assessment by artificial neural networks for hepatocellular carcinoma patients after radiofrequency ablation, Journal of the Formosan Medical Association 116, 765-773.
• Vallières, M. et al. (2017). Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer, Sci Rep 10117 doi: 10.1038/s41598-017-10371-5.
• Vapnik, V. N. (1995). The Nature of Statistical Learning Theory, 1st Edition, Springer- Verlag, New York USA
• Vapnik, V. N. (2000) The Nature of Statistical Learning Theory, 2nd Edition, Springer- Verlag, New York.
• Young, D., Xiao, C. C., Murphy, B., Moore, M., Fakhry, C., Day, T. A. (2015). Increase in head and neck cancer in younger patients due to human papillomavirus (HPV), Oral Oncology, 51, 727–730.
• Yu, Z., Chen, H., Liu, J., You, J., Leung, H., and Han, G. (2016). Hybrid, k-Nearest Neighbor Classifier, IEEE Transactions Cybernetics, 46, 1263-1275
• Zheng, H., Chen, L., Han, X., Zhao, X., Ma, Y. (2009). Classification and regression tree (CART) for analysis of soybean yield variability among fields in Northeast China: The importance of phosphorus application rates under drought conditions, Agriculture, Ecosystems & Environment, 132, 98-105.
• Zhou, C., Wang, L., Zhang, Q., Wei, X. (2014). Face recognition based on PCA and logistic regression analysis, Optik, 125, 5916–5919.
• Zini, E. M., Lanzola G., and Quaglini, S. (2017). Detection and Management of Side Effects in Patients with Head and Neck Cancer, IEEE 3rd International Forum on Research and Technologies for Society and Industry (RTSI), 1-6.