Machine Learning-Based Hybrid Recommendation (SVOF-KNN) Model For Breast Cancer Coimbra Dataset Diagnosis

Main Article Content

Ravi Kumar Barwal
Neeraj Raheja
Malika Bhiyana
Dimple Rani

Abstract

An effective way to identify breast cancer is by creating a prediction algorithm using risk factors. Models for ML have been used to improve the effectiveness of early detection. This article analyses a KNN combined with singular value decomposition and Grey wolf optimization(GWO) method to give a detection of breast cancer(BC) at the early phase depending on risk metrics. The SVD technique was utilized to eliminate the reliable feature vectors, the GW optimizer was used to select the feature vectors, and while KNN model was used to diagnose the BC status. The proposed hybrid recommendation model (SVOF-KNN) for BC prediction's main objective is to give an accurate recommendation for BC prognosis through four different steps such as;BCCD dataset collection, data pre-processing, feature selection, and classification/recommendation. It is implemented to classify the consequence of risk metrics connected withregular blood analysis(BA) in the BCCD database. The aspects of the BC dataset are insulin, glucose, HOMA, Leptin, resistin, etc. The error categories such as RMSE and MAE are used to calculate the exception values for each instance of the BC dataset. It hybrid model has recommended the best score instance having the minimumexception rateas the defined features for BC prediction. It improves significance in automatic BC classification with the optimum solution. The hybrid recommendation model (SVOF-KNN) also recommends the accurateclassification method for BC diagnosis. The results of this work shall enhance the QoS in BC care.

Article Details

How to Cite
Barwal, R. K. ., N. . Raheja, M. . Bhiyana, and D. . Rani. “Machine Learning-Based Hybrid Recommendation (SVOF-KNN) Model For Breast Cancer Coimbra Dataset Diagnosis”. International Journal on Recent and Innovation Trends in Computing and Communication, vol. 11, no. 1s, Jan. 2023, pp. 23-42, doi:10.17762/ijritcc.v11i1s.5991.
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References

Alfian, G., Syafrudin, M., Fitriyani, N. L., Anshari, M., Stasa, P., Svub, J., & Rhee, J. (2020). Deep neural network for predicting diabetic retinopathy from risk factors. Mathematics, 8(9), 1620.

Alfian, G., Syafrudin, M., Fitriyani, N. L., Syaekhoni, M. A., & Rhee, J. (2021). Utilizing IoT-based sensors and prediction model for health-care monitoring system. In Artificial Intelligence and Big Data Analytics for Smart Healthcare (pp. 63-80). Academic Press.

Fitriyani, N. L., Syafrudin, M., Alfian, G., & Rhee, J. (2019). Development of disease prediction model based on ensemble learning approach for diabetes and hypertension. IEEE Access, 7, 144777-144789.

Fitriyani, N. L., Syafrudin, M., Alfian, G., Fatwanto, A., Qolbiyani, S. L., & Rhee, J. (2020, November). Prediction Model for Type 2 Diabetes using Stacked Ensemble Classifiers. In 2020 International Conference on Decision Aid Sciences and Application (DASA) (pp. 399-402). IEEE.

Ferlay, J., Soerjomataram, I., Dikshit, R., Eser, S., Mathers, C., Rebelo, M., ... & Bray, F. (2015). Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. International journal of cancer, 136(5), E359-E386.

Breast Cancer. Available online: https://www.who.int/news-room/fact-sheets/detail/breast-cancer (accessed on 22 November 2022).

Magny, S. J., Shikhman, R., & Keppke, A. L. (2022). Breast imaging reporting and data system. In StatPearls [Internet]. StatPearls publishing.

Williams, K., Idowu, P. A., Balogun, J. A., & Oluwaranti, A. I. (2015). Breast cancer risk prediction using data mining classification techniques. Transactions on Networks and Communications, 3(2), 01.

Durai, S. G., Ganesh, S. H., & Christy, A. J. (2016). Prediction of breast cancer through classification algorithms: a survey. International Science Press, 9(27), pp. 359-65.

Aavula, R., Bhramaramba, R., & Ramula, U. S. (2019). A Comprehensive Study on Data Mining Techniques used in Bioinformatics for Breast Cancer Prognosis. Journal of Innovation in Computer Science and Engineering, 9(1), 34-39.

Kaushik, D., & Kaur, K. (2016, July). Application of Data Mining for high accuracy prediction of breast tissue biopsy results. In 2016 Third International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC) (pp. 40-45). IEEE.

Mokhtar, S. A., & Elsayad, A. (2013). Predicting the severity of breast masses with data mining methods. arXiv preprint arXiv:1305.7057.

Chaurasia, V., Pal, S., & Tiwari, B. B. (2018). Prediction of benign and malignant breast cancer using data mining techniques. Journal of Algorithms & Computational Technology, 12(2), 119-126.

Fan, J., Wu, Y., Yuan, M., Page, D., Liu, J., Ong, I. M., ... & Burnside, E. (2016). Structure-leveraged methods in breast cancer risk prediction. The Journal of Machine Learning Research, 17(1), 2956-2970.

Huang, Y. L., Chen, J. H., & Shen, W. C. (2006). Diagnosis of hepatic tumors with texture analysis in nonenhanced computed tomography images. Academic radiology, 13(6), 713-720.

Rabiei, R., Ayyoubzadeh, S. M., Sohrabei, S., Esmaeili, M., & Atashi, A. (2022). Prediction of Breast Cancer using Machine Learning Approaches. Journal of Biomedical Physics and Engineering, 12(3), 297-308.

Ahmad, F. K., & Yusoff, N. (2013, December). Classifying breast cancer types based on fine needle aspiration biopsy data using random forest classifier. In 2013 13th International Conference on Intellient Systems Design and Applications (pp. 121-125).IEEE.

Fatima, N., Liu, L., Hong, S; Ahmed, H. (2020). Prediction of breast cancer, comparative review of machine learning techniques,and their analysis. IEEE Access, 8, 150360-150376.

Hou, R., Mazurowski, M. A., Grimm, L. J., Marks, J. R., King, L. M., Maley, C. C., ... ,Lo, J. Y. (2019). Prediction of upstaged ductalcarcinoma in situ using forced labeling and domain adaptation. IEEE Transactions on Biomedical Engineering, 67(6), 1565-1572.

Brinton, L. A., Sherman, M. E., Carreon, J. D; Anderson, W. F. (2008). Recent trends in breast cancer among younger women in theUnited States. JNCI: Journal of the National Cancer Institute, 100(22), 1643-1648.

Virnig, B. A., Tuttle, T. M., Shamliyan, T., Kane, R. L. (2010). Ductal carcinoma in situ of the breast: a systematic review ofincidence, treatment, and outcomes. Journal of the National Cancer Institute, 102(3), 170-178.

Pervez, S.; Khan, H. (2007). Infiltrating ductal carcinoma breast with central necrosis closely mimicking ductal carcinoma in situ(comedo type): a case series. Journal of medical case reports, 1(1), 1-4.

Page, D. L., Dupont, W. D., Rogers, L. W., Landenberger, M. (1982). Intraductal carcinoma of the breast: follow?up after biopsyonly. Cancer, 49(4), 751-758.

Chaudhury, A. R., Iyer, R., Iychettira, K. K; Sreedevi, A. (2011, November). Diagnosis of invasive ductal carcinoma using imageprocessing techniques.In 2011 International Conference on Image Information Processing (pp. 1-6).IEEE.

Tuck, A. B., O;Malley, F. P., Singhal, H., Tonkin, K. S. (1997). Osteopontin and p53 expression are associated with tumorprogression in a case of synchronous, bilateral, invasive mammary carcinomas. Archives of pathology, laboratory medicine, 121(6), 578.

Lee, B., Kim, K., Choi, J. Y., Suh, D. H., No, J. H., Lee, H. Y.; Kim, Y. B. (2017). Efficacy of the multidisciplinary tumor boardconference in gynecologic oncology: a prospective study. Medicine, 96(48).

Rajan, S., Foreman, J., Wallis, M. G., Caldas, C., Britton, P. (2013). Multidisciplinary decisions in breast cancer: does the patientreceive what the team has recommended?. British journal of cancer, 108(12), 2442-2447.

Masciari, S., Larsson, N., Senz, J., Boyd, N., Kaurah, P., Kandel, M. J., Huntsman, D. (2007). Germline E-cadherin mutations infamilial lobular breast cancer. Journal of medical genetics, 44(11), 726-731.

Memis, A., Ozdemir, N., Parildar, M., Ustun, E. E.; Erhan, Y. (2000). Mucinous (colloid) breast cancer: mammographic and USfeatures with histologic correlation. European journal of radiology, 35(1), 39-43.

Gradilone, A., Naso, G., Raimondi, C., Cortesi, E., Gandini, O., Vincenzi, B., Gazzaniga, P. (2011). Circulating tumor cells

(CTCs) in metastatic breast cancer (MBC): prognosis, drug resistance and phenotypic characterization. Annals of Oncology, 22(1), 86-92.

Robertson, F. M., Bondy, M., Yang, W., Yamauchi, H., Wiggins, S., Kamrudin, S., Cristofanilli, M. (2010). Inflammatory breastcancer: the disease, the biology, the treatment. CA: a cancer journal for clinicians, 60(6), 351-375.

Chaurasia, V., Pal, S., & Tiwari, B. B. (2018). Prediction of benign and malignant breast cancer using data mining techniques. Journalof Algorithms Computational Technology, 12(2), 119-126.

Obermeyer, Z., Emanuel, E. J. (2016). Predicting the future—big data, machine learning, and clinical medicine. The New Englandjournal of medicine, 375(13), 1216.

Ming, C., Viassolo, V., Probst-Hensch, N., Chappuis, P. O., Dinov, I. D., & Katapodi, M. C. (2019). Machine learning techniques forpersonalized breast cancer risk prediction: comparison with the BCRAT and BOADICEA models. Breast Cancer Research, 21(1), 1-11.

Chen, H. C., Kodell, R. L., Cheng, K. F.; Chen, J. J. (2012). Assessment of performance of survival prediction models for cancerprognosis. BMC medical research methodology, 12(1), 1-11.

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., Fotiadis, D. I. (2015). Machine learning applications in cancerprognosis and prediction. Computational and structural biotechnology journal, 13, 8-17.

Niknejad, A., Petrovic, D. (2013). Introduction to computational intelligence techniques and areas of their applications in medicine. Med ApplArtifIntell, 51, 2113-19.

Kourou, K., Exarchos, T. P., Exarchos, K. P., Karamouzis, M. V., Fotiadis, D. I. (2015). Machine learning applications in cancerprognosis and prediction. Computational and structural biotechnology journal, 13, 8-17.

Sahoo, A. K., Pradhan, C., Barik, R. K., & Dubey, H. (2019). DeepReco: deep learning based health recommender system using collaborative filtering. Computation, 7(2), 25.

Wiesner, M., & Pfeifer, D. (2014). Health recommender systems: concepts, requirements, technical basics and challenges. Internationaljournal of environmental research and public health, 11(3), 2580-2607.

Alfian, G., Syafrudin, M., Fahrurrozi, I., Fitriyani, N. L., Atmaji, F. T. D., Widodo, T., ...& Rhee, J. (2022). Predicting Breast Cancer from Risk Factors Using SVM and Extra-Trees-Based Feature Selection Method. Computers, 11(9), 136.

Kanimozhi, G., Shanmugavadivu, P., & Rani, M. M. S. (2020). Machine Learning?Based Recommender System for Breast Cancer Prognosis. Recommender System with Machine Learning and Artificial Intelligence: Practical Tools and Applications in Medical, Agricultural and Other Industries, 121-140.

Ahmed, I., Lu, S., Bai, C., & Bhuyan, F. A. (2018, July). Diagnosis recommendation using machine learning scientific workflows. In 2018 IEEE International Congress on Big Data (BigData Congress) (pp. 82-90). IEEE.

Aslan, M. F., Celik, Y., Sabanc?, K., & Durdu, A. (2018). Breast cancer diagnosis by different machine learning methods using blood analysis data. International Journal of Intelligent Systems and Applications in Engineering.

Polat, K., & Sentürk, U. (2018, October). A novel ML approach to prediction of breast cancer: combining of mad normalization, KMC based feature weighting and AdaBoostM1 classifier. In 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-4). Ieee.

Austria, Y. D., Jay-ar, P. L., Maria Jr, L. B. S., Goh, J. E. E., Goh, M. L. I., & Vicente, H. N. (2019). Comparison of machine learning algorithms in breast cancer prediction using the coimbra dataset. cancer, 7(10), 23-1.

Patrício, M., Pereira, J., Crisóstomo, J., Matafome, P., Gomes, M., Seiça, R., & Caramelo, F. (2018). Using Resistin, glucose, age and BMI to predict the presence of breast cancer. BMC cancer, 18(1), 1-8.

Abdulla, Srwa Hasan, Ali Makki Sagheer, and Hadi Veisi. "Breast Cancer Classification Using Machine Learning Techniques: A Review." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 14 (2021): 1970-1979.

Karthik, S., R. Srinivasa Perumal, and P. V. S. S. R. Chandra Mouli. "Breast cancer classification using deep neural networks." In Knowledge computing and its applications, pp. 227-241. Springer, Singapore, 2018.

UCI Machine Learning Repository: Breast Cancer Coimbra Data Set. Available at: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Coimbra# (Accessed: November 21, 2022).

Manh, H. D. Feature Selection Using Singular Value Decomposition And Orthogonal Centroid Feature Selection For Text Classification, International Journal of Research in Engineering and Technology, 5(5),pp:1-5.

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in engineering software, 69, 46-61.