Cervical cancer is a leading cause of women’s mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification—handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women’s health and healthcare systems.

Cervical cancer is a leading cause of women’s mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification—handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women’s health and healthcare systems.

The dataset is created by imputing the missing values of ICR - Identifying Age Related Conditions competition dataset. In this dataset depending on feature selection some subversions are also created. - Version 1 : The version is created by dropping all the rows with missing values. - Version 2 : The version is created by 'BQ' and 'EL' columns which consist most of the missing values. To remove the remaining missing values rows with missing values are deleted. - Version 3 : The version is created by imputing mean values by column average. Median is considered as measure of average. - Version 4 : The version is created by imputing missing values of 'BQ' and 'EL' by linear regression models and remaining missing values are imputed by average value of the column where missing value is present. 'AB', 'AF', 'AH', 'AM', 'CD', 'CF', 'DN', 'FL' and 'GL' are used to calculate the missing values of 'BQ'. 'CU', 'GE' and 'GL' are used to calculate missing values of 'EL'. Models are found in the version4/imputer. Two subversions are created by extraction only important features of the dataset. - Version 5 : The version is created by imputing missing values using KNNImputer. Two subversions are created by extracting only important features. For the categorical feature 'EJ', 'A' is encoded as 0 and 'B' is encoded as '1'. For more details how the transformations of the dataset is done visit this notebook.

📊 Data Overview The original dataset can be found in the CIBMTR competition on Kaggle. Users are encouraged to refer to the original source for context and additional documentation. Primary Focus: The preprocessing focused exclusively on handling missing values without altering feature distributions or introducing additional transformations.

🛠️ Data Cleaning Process Numerical Features:

Missing values in numerical columns were imputed using the KNNImputer after applying MinMaxScaling to ensure consistent imputation across varying ranges.

Categorical Features:

Missing values in categorical columns were addressed using the SimpleImputer with the 'most_frequent' strategy to maintain logical consistency. Special Handling for Key Features:

For the categorical features tce_imm_match, tce_div_match, and tce_match, a custom value mapping approach was applied using the following mappings:

Mapping A:

'P/P' → 'Permissive mismatched' 'G/G' → 'GvH non-permissive' 'H/H' → 'HvG non-permissive'

Mapping B:

'Permissive mismatched' → 'Permissive' 'GvH non-permissive' → 'GvH non-permissive' 'HvG non-permissive' → 'HvG non-permissive'

🔍 Key Notes Imputation Consistency: Numerical and categorical missing values were handled using appropriate techniques to reduce data leakage risks. Scalability: Numerical scaling was performed prior to KNN imputation and can be inverted using the respective scaler for downstream tasks. Traceability: Original mappings and imputation strategies are documented to maintain transparency in data processing steps.

📥 Recommendation For additional insights, refer to the original CIBMTR dataset from the competition. This preprocessed dataset serves as an optimized starting point for predictive modeling and exploratory data analysis.

Cervical cancer is a leading cause of women’s mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification—handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women’s health and healthcare systems.

Cervical cancer is a leading cause of women’s mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification—handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women’s health and healthcare systems.

Cervical cancer is a leading cause of women’s mortality, emphasizing the need for early diagnosis and effective treatment. In line with the imperative of early intervention, the automated identification of cervical cancer has emerged as a promising avenue, leveraging machine learning techniques to enhance both the speed and accuracy of diagnosis. However, an inherent challenge in the development of these automated systems is the presence of missing values in the datasets commonly used for cervical cancer detection. Missing data can significantly impact the performance of machine learning models, potentially leading to inaccurate or unreliable results. This study addresses a critical challenge in automated cervical cancer identification—handling missing data in datasets. The study present a novel approach that combines three machine learning models into a stacked ensemble voting classifier, complemented by the use of a KNN Imputer to manage missing values. The proposed model achieves remarkable results with an accuracy of 0.9941, precision of 0.98, recall of 0.96, and an F1 score of 0.97. This study examines three distinct scenarios: one involving the deletion of missing values, another utilizing KNN imputation, and a third employing PCA for imputing missing values. This research has significant implications for the medical field, offering medical experts a powerful tool for more accurate cervical cancer therapy and enhancing the overall effectiveness of testing procedures. By addressing missing data challenges and achieving high accuracy, this work represents a valuable contribution to cervical cancer detection, ultimately aiming to reduce the impact of this disease on women’s health and healthcare systems.

Results of the ML models were obtained by deleting missing values from the dataset.

5-fold- cross-validation results for the proposed approach.

Performance comparison with state-of-the-art studies.