Survival Prediction of Cervical Cancer Patients using Genetic Algorithm-Based Data Value Metric and Recurrent Neural Network
Ojie, D.V1, Akazue M2, Omede E.U3, Oboh E.O4, Imianvan A5

1Ojie Deborah Voke, Department of Software Engineering, University Delta, C Agbor, Nigeria.
2Dr. Akazue M, Department of Computer Science, Delta State University, Abraka, Nigeria.
3Dr. Omede E. U, Department of Computer Science, Delta State University, Abraka, Nigeria.
4Dr. Oboh E.O, Department of Radiotherapy/ Clinical Oncology, University of Benin Teaching Hospital, Edo State.
5Prof. Imianvan A., Department of Computer Science, University of Benin, Benin, Edo Nigeria.

Manuscript received on 12 April 2023 | Revised Manuscript received on 20 April 2023 | Manuscript Accepted on 15 May 2023 | Manuscript published on 30 May 2023 | PP: 29-41 | Volume-13 Issue-2, May 2023 | Retrieval Number: 100.1/ijsce.B36080513223 | DOI: 10.35940/ijsce.B3608.0513223
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Abstract: Survival analysis and machine learning has been shown to be an indispensable aspect of disease management as it enables practitioners to understand and prioritize treatment mostly in terminal diseases. Cervical cancer is the most common malignant tumor of the female reproductive organ worldwide. Survival analysis which is a time –to –event analysis for survival prediction is therefore needed for cervical cancer patients. Data Value Metric (DVM) is an information theoretic measure which uses the concept of mutual information and has shown to be a good metric for quantifying the quality and utility of data as well as feature selection. This study proposed the hybrid of Genetic Algorithm and Data Value Metric for feature selection while Recurrent Neural Network and Cox Proportionality Hazard ratio was used to build the survival prediction model in managing cervical cancer patients. Dataset of 107 patients of cervical cancer patients were collected from University of Benin Teaching Hospital, Benin, Edo State and was used in building the proposed model (RNN+GA-DVM). The proposed system outperform the existing system as the existing system had accuracy of 70% and ROC score of 0.6041 while the proposed model gave an accuracy of 75.16% and ROC score of 0.7120 respectively. From this study, It was observed using the GA_DVM features selection that the variables highly associated with cervical cancer mortality are age_at_diagnosis, Chemotherapy, Chemoradiation, Histology, Comorbidity, Menopause, and MENO_Post. Thus, with early diagnosis and proper health management of cervical cancer, the age of survival of cervical cancer patients can be prolonged. 
Keywords: Cervical cancer, Cox Proportional Hazard, Machine Learning, Survival Model.
Scope of the Article: Machine Learning