Article: Advancing breast cancer detection: a comprehensive investigation of advanced classification techniques Journal: International Journal of Engineering Systems Modelling and Simulation (IJESMS) 2026 Vol.17 No.2 pp.71 - 82 Abstract: As the most prevalent cancer in women worldwide, breast cancer requires early detection to have the best possible treatment results. However, conventional screening techniques, like mammography and clinical examinations, can take time and effort. In this paper, we proposed a predictive model for the identification of breast cancer by combining state-of-the-art model BERT with machine learning approaches. Several machine learning algorithms, such as K-nearest neighbours, decision tree, random forest, neural network, and BERT, were tested on the Breast Cancer Wisconsin (Diagnostic) Dataset for early prediction of the diseases. The BERT model's accuracy was improved by using hyperparameter optimisation techniques. For the proposed work's evaluation, we used metrics like accuracy, F1-score, precision, and recall on the standard publically available datasets. With an accuracy of 0.98 across various splits and an area under the curve (AUC) of 0.98 in receiver operating characteristic (ROC) curves, our results show that BERT consistently works better than other models. These findings highlight the value of early and reliable identification in improving patient outcomes, highlighting the promise of machine learning algorithms, notably BERT, inaccurate breast cancer prediction. Inderscience Publishers - linking academia, business and industry through research

Title: Advancing breast cancer detection: a comprehensive investigation of advanced classification techniques

Authors: Shilpa Choudhary; Sivaneasan Bala Krishnan; Prasun Chakrabarti

Addresses: Department of Computer Science and Engineering (AIML), Neil Gogte Institute of Technology, Hyderabad, India; Singapore Institute of Technology, Singapore ' Singapore Institute of Technology, Singapore ' Department of Computer Science and Engineering, Sir Padampat Singhania University, Udaipur, Rajasthan, India

Abstract: As the most prevalent cancer in women worldwide, breast cancer requires early detection to have the best possible treatment results. However, conventional screening techniques, like mammography and clinical examinations, can take time and effort. In this paper, we proposed a predictive model for the identification of breast cancer by combining state-of-the-art model BERT with machine learning approaches. Several machine learning algorithms, such as K-nearest neighbours, decision tree, random forest, neural network, and BERT, were tested on the Breast Cancer Wisconsin (Diagnostic) Dataset for early prediction of the diseases. The BERT model's accuracy was improved by using hyperparameter optimisation techniques. For the proposed work's evaluation, we used metrics like accuracy, F1-score, precision, and recall on the standard publically available datasets. With an accuracy of 0.98 across various splits and an area under the curve (AUC) of 0.98 in receiver operating characteristic (ROC) curves, our results show that BERT consistently works better than other models. These findings highlight the value of early and reliable identification in improving patient outcomes, highlighting the promise of machine learning algorithms, notably BERT, inaccurate breast cancer prediction.

Keywords: breast cancer prediction; K-nearest neighbours; KNNs; decision tree; neural networks; random forest; BERT; classification.

DOI: 10.1504/IJESMS.2026.152038

International Journal of Engineering Systems Modelling and Simulation, 2026 Vol.17 No.2, pp.71 - 82

Received: 19 Mar 2024
Accepted: 18 Jul 2024
Published online: 04 Mar 2026 *

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Title: Advancing breast cancer detection: a comprehensive investigation of advanced classification techniques

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