Abstract
Cancer is a very common type of disease occurring amongst people and it is also amongst the main causes of deaths of humans around the world. Symptom awareness and needs of screening are very essential these days in order to reduce its risks. Several machine learning models have already been proposed in order to predict whether cancer is malignant or benign. In this paper, we have attempted to propose a better way to do the same. Here we discuss in detail about how we have applied the chicken swarm Optimisation as a feature selection algorithm to the cancer dataset of features in order to predict if the cancer is malignant or benign. Here we also elucidate how the Chicken Swarm Optimization provides better results than several other machine learning models such as Random Forest, k-NN, Decision Trees and Support Vector Machines. Feature Selection is a technique used to eliminate the redundant features from a large dataset in order to obtain a better subset of features to use for processing. In order to achieve this, we have used Chicken Swarm Optimization. The chicken swarm optimization algorithm is a bio-inspired algorithm. It attempts to mimic the order of hierarchy and the behavior of chicken swarm in order to optimize the problems. On the basis of these predictions we can also provide quick treatment by using virtual reality simulators that can be helpful for complex oncological surgeries. The results shown by this are better than the other models as this model achieves a very high accuracy as compared to the others discussed in the paper.
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References
Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: The next generation. Cell, 144, 646–674.
Ferlay, J., Soerjomataram, I., Ervik, M., Dikshit, R., Eser, S & Mathers, C. (2013) GLOBOCAN 2012 v1.0, cancer incidence and mortality worldwide International Agency for Research on Cancer. IARC CancerBase no 11. Lyon; France. Accessed January 1, 2016.
World Cancer Report. (2008). International agency for research on cancer. Retrieved February 26, 2011.
Polley, M. Y. C., Freidlin, B., Korn, E. L., Conley, B. A., Abrams, J. S., & McShane, L. M. (2013). Statistical and practical considerations for clinical evaluation of predictive biomarkers. Journal of the National Cancer Institute, 105, 1677–1683.
Cruz, J. A., & Wishart, D. S. (2006). Applications of machine learning in cancer prediction and prognosis. Cancer Informatics, 2, 59.
Parham, G., Bing, E. G., Cuevas, A., Fisher, B., Skinner, J., Mwanahamuntu, M., & Sullivan, R. (2019). Creating a low-cost virtual reality surgical simulation to increase surgical oncology capacity and capability. Ecancermedicalscience 13, 910.
Katic, D., Wekerle, A. L., Gortler, J., et al. (2013). Context-aware augmented reality in laparoscopic surgery. Computerized Medical Imaging and Graphics, 37(2), 174–182. https://doi.org/10.1016/j.compmedimag.2013.03.003.
Tyrer, J., Duffy, S. W., & Cuzick, J. (2004). A breast cancer prediction model incorporating familial and personal risk factors. Stat Med, 23(7), 1111–1130.
Moyer, V. A. (2013). Medications to decrease the risk for breast cancer in women: Recommendations from the U.S. preventive services task force recommendation statement. Annals of Internal Medicine, 159(10), 698–708.
Asri, 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. ISSN 1877-0509.
Ahmad, L. G., Eshlaghy, A. T., Poorebrahimi, A., Ebrahimi, M., & Razavi, A. R. (2013). Using three machine learning techniques for predicting breast cancer recurrence. Journal of Health and Medical Informatics, 4, 124. https://doi.org/10.4172/2157-7420.1000124.
Mihaylov, I., Nisheva, M., & Vassilev, D. (2019). Application of machine learning models for survival prognosis in breast cancer studies. Information, 10, 93.
Ramaswami, M., & Bhaskaran, R. (2009). A study on feature selection techniques in educational data mining. arXiv preprint arXiv:0912.3924.
Liu, H., & Yu, L. (2005). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(3), 1–12.
Genetic Algorithm. https://www.geeksforgeeks.org/genetic-algorithms/.
Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing (Vol. 53). Berlin: Springer.
Abo-Hammour, Z. S., Alsmadi, O. M., & Al-Smadi, A. M. (2011). Frequency-based model order reduction via genetic algorithm approach. In 7th International Workshop on Systems, Signal Processing and their Applications (WOSSPA).
Mohamed, K. S. (2018) Bio-inspired machine learning algorithm: Genetic algorithm. In Machine learning for model order reduction (pp 19–34). Cham: Springer.
Xue, B., Zhang, M., Browne, W. N., & Yao, X. (2016). A survey on evolutionary computation approaches to feature selection. IEEE Transactions on Evolutionary Computation, 20(4), 606–626.
Evolutionary Algorithm as Feature Selection. https://www.kdnuggets.com/2017/11/rapidminer-evolutionary-algorithms-feature-selection.html.
Meng, X. B., Yu, L., Gao, X., & Zhang, H. (2014). A new bio-inspired algorithm: Chicken swarm optimization. pp. 86–94. https://doi.org/10.1007/978-3-319-11857-4_10.
Das, S., & Suganthan, P. N. (2011). Differential evolution: A survey of the state-of-the-art. IEEE Transactions on Evolutionary Computation, 15(1), 4–31.
Yang, X. S. (2013). Bat algorithm: Literature review and applications. International Journal of Bio-Inspired Computation, 5(3), 141–149.
Gandomi, A. H., & Alavi, A. H. (2012). Krill herd: A new bio-inspired optimization algorithm. Communications in Nonlinear Science and Numerical Simulation, 17, 4831–4845.
Cuevas, E., Cienfuegos, M., Zaldivar, D., & Cisneros, M. (2013). A swarm optimization algorithm inspired in the behavior of the social-spider. Expert Systems with Applications, 40, 6374–6384.
Kumar, S., Nayyar, A., & Kumari, R. (2019). Arrhenius artificial bee colony algorithm. In S. Bhattacharyya, A. Hassanien, D. Gupta, A. Khanna, I. Pan (Eds.) International conference on innovative computing and communications. Lecture notes in networks and systems (Vol. 56) Singapore: Springer.
Wang, J., Neskovic, P., & Cooper, L. N. (2007). Improving nearest neighbor rule with a simple adaptive distance measure. Pattern Recognition Letters, 28(2), 7.
Zhou, Y., Li, Y., & Xia, S. (2009). An improved KNN text classification algorithm based on clustering. Journal of Computers, 4(3), 8.
KNN. https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm.
Decision Trees. https://en.wikipedia.org/wiki/C4.5_algorithm.
Quinlan, J. R. (2014). C4.5: Programs for machine learning (Vol. 302). https://books.google.com/books?hl=fr&lr=&id=b3ujBQAAQBAJ&pgis=1. Accessed January 5, 2016.
Random Forests. https://en.wikipedia.org/wiki/Random_forest.
Berk, R. A. (2016). Random forests, statistical learning from a regression perspective (pp. 205–258). Cham: Springer.
Noble, W. S. (2006). What is a support vector machine? Nature Biotechnology, 24(12), 1565–1567. https://doi.org/10.1038/nbt1206-1565.
Support Vector Machine. https://en.wikipedia.org/wiki/Support-vector_machine.
Wisconsin Diagnostic Breast Cancer Dataset. http://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+%28diagnostic%29.
Street, W. N., Wolberg, W. H., & Mangasarian, O. L. (1993) Nuclear feature extraction for breast tumor diagnosis. In IS&T/SPIE 1993 International Symposium on Electronic Imaging: Science and Technology (Vol. 1905, pp. 861–870), San Jose, CA.
Bennett, K. P. (1992) Decision tree construction via linear programming. In Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97–101.
Bennett, K. P., & Mangasarian, O. L. (1992). Robust linear programming discrimination of two linearly inseparable sets. Optimization methods and software, 1, 23–34.
Antos, A., Kégl, B., Linder, T., & Lugosi, G. (2002). Data-dependent margin-based generalization bounds for classification. Journal of Machine Learning Research, 3, 73–98.
Bradley, P. S., Bennett, K. P., & Demiriz, A. (2000). Constrained k-means clustering. Microsoft Res Redmond (Microsoft Research Dept. of Mathematical Sciences One Microsoft Way Dept. of Decision Sciences and Eng. Sys).
Cervical cancer Dataset. https://archive.ics.uci.edu/ml/datasets/Cervical+cancer+%28Risk+Factors%29.
Fernandes, K., Cardoso, J. S., & Fernandes, J. (2017). Transfer learning with partial observability applied to cervical cancer screening. In Iberian conference on pattern recognition and image analysis. Cham: Springer.
Heat Map. https://en.wikipedia.org/wiki/Heat_map.
Ünlerşen, Muhammed, Sabanci, Kadir, & Ozcan, Muciz. (2017). Determining cervical cancer possibility by using machine learning methods. International Journal of Recent Technology and Engineering, 3, 65–71.
Dwivedi, R. K., Aggarwal, M., Keshari, S. K., & Kumar, A. (2019). Sentiment analysis and feature extraction using rule-based model (RBM). In S. Bhattacharyya, A. Hassanien, D. Gupta, A. Khanna & Pan, I (Eds.) International conference on innovative computing and communications. Lecture notes in networks and systems (Vol. 56). Singapore: Springer.
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Tripathi, A.K., Garg, P., Tripathy, A., Vats, N., Gupta, D., Khanna, A. (2020). Application of Chicken Swarm Optimization in Detection of Cancer and Virtual Reality. In: Gupta, D., Hassanien, A., Khanna, A. (eds) Advanced Computational Intelligence Techniques for Virtual Reality in Healthcare. Studies in Computational Intelligence, vol 875. Springer, Cham. https://doi.org/10.1007/978-3-030-35252-3_9
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