Improving prediction of cervical cancer using KNN imputer and multi-model ensemble learning

• Published in: PloS one Vol. 19; no. 1; p. e0295632
• Main Author: Aljrees, Turki
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.01.2024; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Artificial intelligence; Cancer; Cancer therapies; Care and treatment; Cellular biology; Cervical cancer; Cervix; Computer and Information Sciences; Datasets; Diagnosis; Disease; Electronic health records; Engineering and Technology; Ensemble learning; Evaluation; Female; Forecasts and trends; Health aspects; Human error; Human papillomavirus; Humans; Illnesses; Industrialized nations; Learning algorithms; Machine Learning; Medical diagnosis; Medical tests; Medicine and Health Sciences; Mental Recall; Missing data; Mortality; Oncology, Experimental; Pap smear; Physical Sciences; Public health; Research and Analysis Methods; Research Design; Support vector machines; Testing procedures; Uterine Cervical Neoplasms - diagnosis; Women; Womens health; Saudi Arabia; Europe
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0295632

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.