A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI
Cervical cancer is a cancer that remains a significant global health challenge all over the world. Due to improper screening in the early stages, and healthcare disparities, a large number of women are suffering from this disease, and the mortality rate increases day by day. Hence, in these studies,...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Journal of Pathology Informatics |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2153353924000373 |
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| author | Rashiduzzaman Shakil Sadia Islam Bonna Akter |
| author_facet | Rashiduzzaman Shakil Sadia Islam Bonna Akter |
| author_sort | Rashiduzzaman Shakil |
| collection | DOAJ |
| description | Cervical cancer is a cancer that remains a significant global health challenge all over the world. Due to improper screening in the early stages, and healthcare disparities, a large number of women are suffering from this disease, and the mortality rate increases day by day. Hence, in these studies, we presented a precise approach utilizing six different machine learning models (decision tree, logistic regression, naïve bayes, random forest, k nearest neighbors, support vector machine), which can predict the early stage of cervical cancer by analysing 36 risk factor attributes of 858 individuals. In addition, two data balancing techniques—Synthetic Minority Oversampling Technique and Adaptive Synthetic Sampling—were used to mitigate the data imbalance issues. Furthermore, Chi-square and Least Absolute Shrinkage and Selection Operator are two distinct feature selection processes that have been applied to evaluate the feature rank, which are mostly correlated to identify the particular disease, and also integrate an explainable artificial intelligence technique, namely Shapley Additive Explanations, for clarifying the model outcome. The applied machine learning model outcome is evaluated by performance evaluation matrices, namely accuracy, sensitivity, specificity, precision, f1-score, false-positive rate and false-negative rate, and area under the Receiver operating characteristic curve score. The decision tree outperformed in Chi-square feature selection with outstanding accuracy with 97.60%, 98.73% sensitivity, 80% specificity, and 98.73% precision, respectively. During the data imbalance, DT performed 97% accuracy, 99.35% sensitivity, 69.23% specificity, and 97.45% precision. This research is focused on developing diagnostic frameworks with automated tools to improve the detection and management of cervical cancer, as well as on helping healthcare professionals deliver more efficient and personalized care to their patients. |
| format | Article |
| id | doaj-art-4e7b8f18f34c48d992eb3ebab085ce24 |
| institution | OA Journals |
| issn | 2153-3539 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pathology Informatics |
| spelling | doaj-art-4e7b8f18f34c48d992eb3ebab085ce242025-08-20T02:35:39ZengElsevierJournal of Pathology Informatics2153-35392024-12-011510039810.1016/j.jpi.2024.100398A precise machine learning model: Detecting cervical cancer using feature selection and explainable AIRashiduzzaman Shakil0Sadia Islam1Bonna Akter2Corresponding author.; Department of Computer Science and Engineering, Daffodil International University, Dhaka, Birulia 1216, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Dhaka, Birulia 1216, BangladeshDepartment of Computer Science and Engineering, Daffodil International University, Dhaka, Birulia 1216, BangladeshCervical cancer is a cancer that remains a significant global health challenge all over the world. Due to improper screening in the early stages, and healthcare disparities, a large number of women are suffering from this disease, and the mortality rate increases day by day. Hence, in these studies, we presented a precise approach utilizing six different machine learning models (decision tree, logistic regression, naïve bayes, random forest, k nearest neighbors, support vector machine), which can predict the early stage of cervical cancer by analysing 36 risk factor attributes of 858 individuals. In addition, two data balancing techniques—Synthetic Minority Oversampling Technique and Adaptive Synthetic Sampling—were used to mitigate the data imbalance issues. Furthermore, Chi-square and Least Absolute Shrinkage and Selection Operator are two distinct feature selection processes that have been applied to evaluate the feature rank, which are mostly correlated to identify the particular disease, and also integrate an explainable artificial intelligence technique, namely Shapley Additive Explanations, for clarifying the model outcome. The applied machine learning model outcome is evaluated by performance evaluation matrices, namely accuracy, sensitivity, specificity, precision, f1-score, false-positive rate and false-negative rate, and area under the Receiver operating characteristic curve score. The decision tree outperformed in Chi-square feature selection with outstanding accuracy with 97.60%, 98.73% sensitivity, 80% specificity, and 98.73% precision, respectively. During the data imbalance, DT performed 97% accuracy, 99.35% sensitivity, 69.23% specificity, and 97.45% precision. This research is focused on developing diagnostic frameworks with automated tools to improve the detection and management of cervical cancer, as well as on helping healthcare professionals deliver more efficient and personalized care to their patients.http://www.sciencedirect.com/science/article/pii/S2153353924000373Cervical cancerSMOTEADASYNChi-squareLASSOMachine learning |
| spellingShingle | Rashiduzzaman Shakil Sadia Islam Bonna Akter A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI Journal of Pathology Informatics Cervical cancer SMOTE ADASYN Chi-square LASSO Machine learning |
| title | A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI |
| title_full | A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI |
| title_fullStr | A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI |
| title_full_unstemmed | A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI |
| title_short | A precise machine learning model: Detecting cervical cancer using feature selection and explainable AI |
| title_sort | precise machine learning model detecting cervical cancer using feature selection and explainable ai |
| topic | Cervical cancer SMOTE ADASYN Chi-square LASSO Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2153353924000373 |
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