Reply to the reviewer comment “

3.      While the Related Work section includes an extensive literature review, the depth of analysis is insufficient, and the current format (e.g., the table) compromises readability. It is recommended to restructure the section by selecting a few representative works from each category—traditional ML, deep learning, and ensemble methods—and analyze their methodologies, contributions, and limitations. The table may be removed to reduce redundancy and improve narrative depth.


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Thank him and rewrite 
This section provides an overview of previous studies and advancements in the field of breast cancer diagnosis using machine learning and deep learning techniques. It highlights various approaches that have been explored, including traditional machine learning models, deep learning architectures, and ensemble methods. Additionally, the section discusses the significance of feature selection and data preprocessing techniques, which play a crucial role in improving model accuracy and interpretability. 

In recent years, advanced ensemble techniques such as Random Forest, Gradient Boosting, XGBoost, and LightGBM have been increasingly incorporated into breast cancer diagnostic studies, enhancing prediction performance and robustness [7] [8] [9]. Parallel to this, deep learning architectures—including convolutional neural networks (CNN), recurrent neural networks (RNN), and long short-term memory (LSTM) networks—have gained significant traction over the past decade for their effectiveness in breast cancer detection [10] [11] [12]. A comprehensive comparative study by Ebrahim, Sedky, and Mesbah (2023) further evaluated the performance of various machine learning models in breast cancer diagnosis. Rasool et al. [13] proposed a Data Exploration Technique (DET) comprising four stages: feature distribution analysis, correlation assessment, feature elimination, and hyperparameter tuning. This DET process was applied to the Breast Cancer Wisconsin (Diagnostic) dataset (BCWD) and the Breast Cancer Coimbra Dataset (BCCD). They developed four predictive models, with polynomial SVM achieving the highest accuracy of 99.3%, followed by logistic regression (98.06%), k-nearest neighbors (97.35%), and an ensemble classifier (97.61%).

Naseem et al. [8] investigated artificial neural networks (ANN) and classifier ensembles for breast cancer diagnosis and prognosis. Their experiments included multiple ensemble and machine learning models, both with and without oversampling, evaluated on two benchmark datasets. The ensemble method outperformed others, reaching an accuracy of 98.83%. Swassen and Okba [14] introduced an expert system combining Radial Basis Function Kernel Extreme Learning Machines (RBF-KELM) with Differential Evolution (DE) for feature optimization. Their DE-RBF-KELM approach was tested on the MIAS and BCWD datasets, showing improved performance over conventional methods.

Wu and Hicks [15] focused on RNA sequencing data to differentiate between triple-negative and non-triple-negative breast cancer using 992 and 110 tumor samples, respectively. They applied Naïve Bayes, SVM, k-NN, and decision trees for classification, concluding that SVM achieved the best performance with minimal misclassification. Al-Azzam and Shatnawi [16] compared supervised and semi-supervised machine learning models on the Wisconsin Diagnosis Cancer dataset. Using just 50% of the labeled data, semi-supervised learning still achieved high accuracy (90%–98%). Among the models, supervised k-NN and semi-supervised logistic regression performed best. Boeri et al. [17] analyzed postoperative data from 610 breast cancer patients out of a total of 1,021. They developed classification models using artificial neural networks and SVMs, achieving accuracies between 95.29% and 96.86%. Lou et al. [6] evaluated recurrence prediction within 10 years after surgery using data from 1,140 patients split into training, internal, and external validation sets. Their study revealed that demographic, clinical, and quality-of-care variables significantly contributed to recurrence risk. Table 1 presents a summary of various breast cancer classification methods derived from pertinent research [18].

Table 1. Summary of Relevant Research on BC Classification
Ref	Year	Method	Dataset	Feature Selection	Feature Extraction	 Result and Accuracy 
[19]
2013	KNN	WBCD	-	-	 Euclidean and Manhattan distances yielded accuracies of 98.70% and 98.48%, respectively. 
[20]
2015	NB, weighted-NB	WBCD	-	-	 Weighted-NB achieved an accuracy of 98.54%. 
[21]
2017	SVM, KNN	MIAS	-	GLCM	 SVM achieved 95.7% accuracy, outperforming KNN. 
[22]
2017	BF, SVM, SLR, NB, KNN, AdaBoost, Fuzzy, DT-J48	699 instances-UCI	Probability technique	-	 SVM achieved the highest accuracy of 97.07%. 
[23]
2018	KNN, NB	683 samples-UCI	Probability technique	-	 KNN achieved 97.51% accuracy. 
[24]
2018	NB, KNN, DT	WBCD	PSO (Particle Swarm Optimization)	-	 Without PSO, DT provided the highest accuracy (76.3%). With PSO, NB achieved 81.3% accuracy. 
[25]
2018	ANN, SVM, KNN	250 mammogram DDSM	T-test algorithm	Wavelet transform	 ANN achieved 98.9% accuracy, outperforming SVM and KNN. 
[26]
2018	KNN	120 images Mini-MIAS	-	GLCM	 KNN achieved 92% accuracy on this dataset. 
[27]
2018	SVM, DT, NB, KNN	357 dataset WBCD	FNA biopsy image features	-	 SVM achieved 92% accuracy, outperforming DT, NB, and KNN. 
[28]
2019	SVM, KNN	30 images-DDSM	-	Contrast Correlation	 KNN achieved the highest accuracy of 97%. 
[29]
2019	KNN, ANN	50 CESM images BAHAYA foundation	-	Curve fitting	 ANN achieved 92% accuracy; KNN achieved 96%. 
[30]
2020	KNN, LR, EL	569 instances-WDBC	Statistical technique (correlation coefficient)	PCA	 EL (ensemble learning) achieved 99.30% accuracy. 
[31]
2020	KNN, DT, SVM, AdaBoost	569 instances-WDBC	NCA	-	 KNN achieved 99.12% accuracy. 
[32]
2020	SVM, DT, NB, LR, LDA, KNN	569 instances - WDBC	-	PCA	 LR achieved 97.23% accuracy; SVM achieved the highest accuracy of 98%. 
[33]
2020	VGG16, VGG19, ResNet50	BreakHis	-	Pre-trained Network	 VGG16 + SVM provided the best result with 80% accuracy. 
[34]
2021	SVM, KNN, NB, Perceptron ML, LR	WBCD	Univariate selection	Recursive feature elimination	 SVM, KNN, Naive Bayes, Perceptron, and LR achieved accuracies of 98.8%, 98.357%, 96.985%, 98.049%, and 98.968%, respectively. With feature reduction, LR achieved the best accuracy (99.968%). 
[35]
2022	CNN	Wide-scale data	-	-	 The proposed DL architecture achieved an average accuracy of 98%. 
[36]
2023	SVM, KNN, NB, EL, DT	WBCD, MBCD	LASSO	-	 SVM achieved the highest accuracy for WBCD and MBCD (97.95%, 98.95%).