Abstract
In the arena of cancer research, it has been able to discover cancer patients utilizing genetic factor information grouped together with artificial intelligence innovations. To strengthen the foretelling paradigms, it is essential to integrate biological, social, and demographic data. In our research article, the gradient boost technique, the (SVM) support vector machine classier, the (LR) logistic regression technique, random forest, the (DT) decision tree classifier, k nearest neighbour, XGBoost, and adaptive linear neuron classifiers have been separately evaluated. In the suggested model, primary objective is to choose the most suitable features. By using correlation matrix and principal component analysis (PCA), we scaled down features for the better enhancement of the model’s efficacy. It gave us 10 important features from the dataset. We have successfully achieved 100% accuracy in the prediction through gradient boost and XGBoost classifier with a 97.50% cross-validation score.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
El-Toukhy S, El-Daly S, Kamel M, Hanafi H (2022) The diagnostic significance of circulating miRNAs and metabolite profiling in early prediction of breast cancer in Egyptian women. J Cancer Res Clin Oncol 149(8):5437–5451
Shaikh FJ, Rao DS (2022) Prediction of cancer disease using machine learning approach. Mater Today Proc 50:40–47
Islam MM, Haque MR, Iqbal H, Hasan MM, Hasan M, Kabir MN (2020) Breast cancer prediction: a comparative study using machine learning techniques. SN Comput Sci 1:1–14
Siddiqui SY, Haider A, Ghazal TM, Khan MA, Naseer I, Abbas S, Ateeq K (2021) IoMT cloud-based intelligent prediction of breast cancer stages empowered with deep learning. IEEE Access 9:146478–214649
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
Thomas T, Pradhan N, Dhaka VS (2020) Comparative analysis to predict breast cancer using machine learning algorithms: a survey. In: 2020 international conference on inventive computation technologies (ICICT). IEEE, pp 192–196
Gupta P, Garg S (2020) Breast cancer prediction using varying parameters of machine learning models. Proc Comput Sci 171:593–601
Elsadig MA, Altigani A, Elshoush HT (2023) Breast cancer detection using machine learning approaches: a comparative study. Int J Electr Comput Eng 13(1). ISSN 2088-8708
Naji MA, El Filali S, Aarika K, Benlahmar EH, Abdelouhahid RA, Debauche O (2021) Machine learning algorithms for breast cancer prediction and diagnosis. Proc Comput Sci 191:487–492
Hosni M, Abnane I, Idri A, de Gea JMC, Alemán JLF (2019) Reviewing ensemble classification methods in breast cancer. Comput Methods Program Biomed 177:89–112
Asri H, Mousannif H, Al Moatassime H, Noel T (2016) Using machine learning algorithms for breast cancer risk prediction and diagnosis. Proc Comput Sci 83:1064–1069
Singh P, Mohan P, Rajput R (2023) Combining K-means and Gaussian mixture model for better accuracy in prediction of ductal carcinoma in situ (DCIS)-breast cancer. In: IEEE international conference on integrated circuits and communication systems (ICICACS)
Sharma P, Narawade V, Mahadik A (2023) A comparison and analysis of supervised machine learning algorithms towards accurate predicting of heart diseases. Biogecko 12(1):1–8
Mahadik A, Sharma P, Narawade V (2023) Prediction and analysis of diabetes using machine learning. J Data Acquisit Process 38(2):1330–1341
Singh G (2020) Breast cancer prediction using machine learning. Int J Sci Res Comput Sci Eng Inf Technol 8(4):278–284
Bayrak EA, Kırcı P, Ensari T (2019) Comparison of machine learning methods for breast cancer diagnosis. In: 2019 scientific meeting on electrical-electronics and biomedical engineering and computer science (EBBT). IEEE, pp 1–3
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mahadik, A., Sharma, P., Narawade, V. (2024). Breast Cancer Prediction with Gradient Boost and XGBoost. In: Devi, B.R., Kumar, K., Raju, M., Raju, K.S., Sellathurai, M. (eds) Proceedings of Fifth International Conference on Computer and Communication Technologies. IC3T 2023. Lecture Notes in Networks and Systems, vol 897. Springer, Singapore. https://doi.org/10.1007/978-981-99-9704-6_3
Download citation
DOI: https://doi.org/10.1007/978-981-99-9704-6_3
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-9703-9
Online ISBN: 978-981-99-9704-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)