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@article{177593,
        author = {T Jahnavi and Ravula Vinay Kumar and Sayyada Ifrah and Saroja Kumar Rout},
        title = {Prediction Of Cervical Cancer Using Genetic Algorithm and Particle Swarm Optimization Algorithm, Machine Learning Technqiues},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {4787-4792},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177593},
        abstract = {Cancer remains a significant global health challenge, necessitating accurate predictive models for early diagnosis. This study proposes a hybrid approach integrating Genetic Algorithm (GA) and Particle Swarm Optimisation (PSO) for feature selection, optimising predictive performance while reducing dimensionality. The selected features train Support Vector Machine (SVM), Random Forest (RF), and Decision Tree (DT) models. Additionally, a hybrid ensemble model using voting and stacking techniques enhances classification accuracy. This approach leverages bio-inspired optimisation and machine learning to provide an efficient predictive framework for cervical cancer diagnosis. The proposed model can aid in early prediction, improving clinical decision-making and patient outcomes.},
        keywords = {Cervical cancer, Genetic Algorithm, Particle Swarm Optimisation, Machine Learning, Prediction, Feature Selection, Classification Models},
        month = {March},
        }