Novel ensemble learning approach with SVM-imputed ADASYN features for enhanced cervical cancer prediction

Cervical cancer remains a leading cause of female mortality, particularly in developing regions, underscoring the critical need for early detection and intervention guided by skilled medical professionals. While Pap smear images serve as valuable diagnostic tools, many available datasets for automated cervical cancer detection contain missing data, posing challenges for machine learning models’ efficacy. To address these hurdles, this study presents an automated system adept at managing missing information using ADASYN characteristics, resulting in exceptional accuracy. The proposed methodology integrates a voting classifier model harnessing the predictive capacity of three distinct machine learning models. It further incorporates SVM Imputer and ADASYN up-sampled features to mitigate missing value concerns, while leveraging CNN-generated features to augment the model’s capabilities. Notably, this model achieves remarkable performance metrics, boasting a 99.99% accuracy, precision, recall, and F1 score. A comprehensive comparative analysis evaluates the proposed model against various machine learning algorithms across four scenarios: original dataset usage, SVM imputation, ADASYN feature utilization, and CNN-generated features. Results indicate the superior efficacy of the proposed model over existing state-of-the-art techniques. This research not only introduces a novel approach but also offers actionable suggestions for refining automated cervical cancer detection systems. Its impact extends to benefiting medical practitioners by enabling earlier detection and improved patient care. Furthermore, the study’s findings have substantial societal implications, potentially reducing the burden of cervical cancer through enhanced diagnostic accuracy and timely intervention.

addressing class imbalances, and extracting complex features.Moreover, limited research has specifically evaluated the effectiveness of integrating Convolutional Neural Networks in conjunction with traditional ML methods for cervical cancer prediction.

Concern 4
What is the motivation and rationale of the study.
Response: The authors highly appreciate your valuable suggestions.Respected reviewer, The motivation and rationale for this study stem from the pressing need within the medical field to enhance the accuracy and efficiency of cervical cancer diagnosis.Existing research has showcased the potential of machine learning techniques, yet there's a critical gap in comprehensively evaluating the combined impact of various methodologies, such as ensemble models, oversampling techniques, and neural network approaches.This study aims to fill this void by proposing a holistic framework that integrates these methods, intending to significantly improve prediction accuracy, handle data complexities, and address imbalances, thereby advancing the field of computer-aided diagnosis for cervical cancer.

Concern 5 Figure quality is not good.
Response: Respected reviewer, We have improved quality of figures.

Concern 6
Empirical analysis of the proposed methodology is missing.
Response: Respected reviewer, We have provided data sources, preprocessing steps, experimental setup, performance metrics, and results.Additionally, comparing the methodology's performance against existing approaches, and discussing the empirical findings, limitations, and potential improvements comprehensively address the empirical analysis.

Concern 7
Authors should re-write the Abstract based on novelty, Challenges, validation techniques, and some suggestion.Impact on social and Clinical.Response: Respected reviewer, We have revised the abstract.

Concern 8
Stop using words like our work and we.Used proposed work or proposed model in the entire paper.
Response: We are thankful for your insightful comment.Respected reviewer, We have removed such words from the paper.

Concern 9
Stick to one term either F1 score or F1-score in the entire paper.
Response: Respected Reviewer, We have corrected it and used F1 score in the paper.

Concern 10
Finally, grammatical proofing also required to improve the interest of the readers.
Ensembling Random Forest (RF), K-Nearest Neighbors (KNN), and Logistic Regression (LR) models offer a strategic advantage by combining their diverse learning approaches to improve predictive performance.This ensemble aims to leverage the strengths of each model: RF's robustness, KNN's pattern recognition, and LR's probabilistic interpretation, thereby enhancing overall accuracy, reducing overfitting, improving robustness against outliers, and providing a more comprehensive analysis of the cervical cancer dataset.The ensemble, facilitated by a voting classifier, fosters a collective decision-making process, resulting in a more robust and accurate predictive model for cervical cancer detection.

Concern 4
Add an example to show how the voting classifier works for ensemble learning.
Response: The authors highly appreciate your valuable suggestions.Respected reviewer, we have added the algorithm with example in the paper at page 7 and 8 in the paper.

Concern 5 Explain with reference, what is class imbalance problem and why it is necessary to solve
Response: Respected reviewer, WE have added an explanation in the paper with the following lines: Class imbalance in datasets emerges when one group considerably outnumbers others, providing issues for ML models by biassing them towards the majority class [2].This imbalance can lead to worse detection of minority groups, for as when identifying illnesses like cancer, affecting model accuracy and patient outcomes.Addressing this imbalance is vital for ensuring fair and accurate learning, eliminating biases towards majority classes, and boosting the model's capacity to recognise all classes successfully, especially in critical areas like medical diagnosis.