Cao W, Chen HD, Yu YW, Li N, Chen WQ. Changing profiles of cancer burden worldwide and in China: a secondary analysis of the global cancer statistics 2020. Chin Med J. 2021;134(07):783–91.
Maurya AP, Brahmachari S. Current status of breast cancer management in India. Indian J Surg. 2021;83(Suppl 2):316–21.
Ganggayah MD, Taib NA, Har YC, Lio P, Dhillon SK. Predicting factors for survival of breast cancer patients using machine learning techniques. BMC Med Inf Decis Mak. 2019;19:1–17.
Mahto D, Yadav SC, Lalotra GS. (2022). Sentiment Prediction of Textual Data Using Hybrid ConvBidirectional-LSTM Model. Mobile Information Systems, 2022.
Dhahri H, Al Maghayreh E, Mahmood A, Elkilani W, Faisal Nagi M. (2019). Automated breast cancer diagnosis based on machine learning algorithms. Journal of healthcare engineering, 2019.
Krishnam NP, Ashraf MS, Rajagopal BR, Vats P, Chakravarthy DSK, Rafi SM. Analysis of current Trends, advances and challenges of Machine Learning (Ml) and knowledge extraction: from ml to explainable AI. Volume 58. Industry Qualifications The Institute of Administrative Management UK; 2022. pp. 54–62.
Chaurasia V, Pal S, Tiwari BB. Prediction of benign and malignant breast cancer using data mining techniques. J Algorithms Comput Technol. 2018;12(2):119–26.
Banu AB, Subramanian PT. Comparison of Bayes classifiers for breast cancer classification. Asian Pac J cancer Prevention: APJCP. 2018;19(10):2917.
Sakri SB, Rashid NBA, Zain ZM. Particle swarm optimization feature selection for breast cancer recurrence prediction. IEEE Access. 2018;6:29637–47.
Yue W, Wang Z, Chen H, Payne A, Liu X. Machine learning with applications in breast cancer diagnosis and prognosis. Designs. 2018;2(2):13.
Juneja K, Rana C. An improved weighted decision tree approach for breast cancer prediction. Int J Inform Technol. 2020;12(3):797–804.
Azar AT, El-Metwally SM. Decision tree classifiers for automated medical diagnosis. Neural Comput Appl. 2013;23:2387–403.
Senapati MR, Panda G, Dash PK. Hybrid approach using KPSO and RLS for RBFNN design for breast cancer detection. Neural Comput Appl. 2014;24:745–53.
Jhajharia S, Verma S, Kumar R. (2016, August). A cross-platform evaluation of various decision tree algorithms for prognostic analysis of breast cancer data. In 2016 International Conference on Inventive Computation Technologies (ICICT) (Vol. 3, pp. 1–7). IEEE.
Hasan MK, Islam MM, Hashem MMA. (2016, May). Mathematical model development to detect breast cancer using multigene genetic programming. In 2016 5th international conference on informatics, electronics and vision (ICIEV) (pp. 574–579). IEEE.
Azar AT, El-Said SA. Performance analysis of support vector machines classifiers in breast cancer mammography recognition. Neural Comput Appl. 2014;24:1163–77.
Chaurasia V, Pandey MK, Pal S. Chronic kidney disease: a prediction and comparison of ensemble and basic classifiers performance. Human-Intelligent Syst Integr. 2022;4(1–2):1–10.
Alloghani M, Al-Jumeily D, Mustafina J, Hussain A, Aljaaf AJ. A systematic review on supervised and unsupervised machine learning algorithms for data science. Supervised and unsupervised learning for data science; 2020. pp. 3–21.
Chaurasia V, Chaurasia A. Novel method of characterization of Heart Disease Prediction using sequential feature selection-based ensemble technique. Biomedical Materials & Devices; 2023. pp. 1–10.
Obulesu O, Mahendra M, ThrilokReddy M. (2018, July). Machine learning techniques and tools: A survey. In 2018 international conference on inventive research in computing applications (ICIRCA) (pp. 605–611). IEEE.
Kumar P, Hati AS. Review on machine learning algorithm based fault detection in induction motors. Arch Comput Methods Eng. 2021;28:1929–40.
Menze BH, Kelm BM, Masuch R, Himmelreich U, Bachert P, Petrich W, Hamprecht FA. A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. BMC Bioinformatics. 2009;10:1–16.
Lalwani P, Mishra MK, Chadha JS, Sethi P. Customer churn prediction system: a machine learning approach. Computing; 2022. pp. 1–24.
Haripriya L, Jabbar MA. (2018, March). Role of machine learning in intrusion detection system. In 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA) (pp. 925–929). IEEE.
Mangla M, Shinde SK, Mehta V, Sharma N, Mohanty SN, editors. Handbook of Research on Machine Learning: foundations and applications. CRC; 2022.
Ji X, Yang B, Tang Q. Acoustic seabed classification based on multibeam echosounder backscatter data using the PSO-BP-AdaBoost algorithm: a case study from Jiaozhou Bay, China. IEEE J Oceanic Eng. 2020;46(2):509–19.
Breast Cancer Wisconsin Data Set. https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+%28Original%29
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