Haraz, A., Elzein, I., Chamseddine, A., & Eltanboly, A.H. (2024). Cervical Cancer Perceived Risk Factors Behavior Using Logistic Regression Technique. Preprints. https://doi.org/10.20944/preprints202403.1220.v1
Chicago/Turabian Style
Haraz, A., Ashraf Chamseddine and Ahmed H. Eltanboly. 2024 "Cervical Cancer Perceived Risk Factors Behavior Using Logistic Regression Technique" Preprints. https://doi.org/10.20944/preprints202403.1220.v1
Abstract
Cervical cancer is one of the ailments that endangers the health of women all over the world and causes infertility. Fortunately, this disease can be avoided. Both the results and participation rates of the current preventative strategy remain poor. Hence, preventative or early detection methods remain challenging and open. In gynecology and computer science fields, few studies on identifying cervical cancer are based on behavior risk factor and machine learning. Moreover, various social and behavioral aspects might often make it more difficult to detect cervical cancer especially in low‑ and middle‑income countries (LMICs). Accordingly, focus on predicting the presence of cervical cancer through behavioral risk factors is the basic structure of this research. We proposed an approach to classify cervical cancer from a social-behavioral perspective using logistic regression to predict cervical cancer. We got significantly improved outcomes than the existing methods. Moreover, we used feature engineering to formulate less dimensionality as a pre-processing step and optimized parameters to be prepared for classification phase. The results proved that using the logistic regression with regularization type L1 had a promising performance. The accuracy of Logistic Regression (LR) L1 was 97.2%. Such results support the proposed work as a reliable classification predictive tool.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
