Accurately predicting health outcomes is significant in medical research in the age of precision medicine (Ahmadzia et al., 2024). Combining statistical methods and the machine learning algorithms in the hybrid models, this study aims to improve the predictive reliability of cervical cancer outcome predictions. The study then allows for a comprehensive analysis of health outcomes by considering both genetic and environmental factors on the data provided by patient clinical data and genomic information (Zhuang et al., 2024). The accuracy of the prediction and its underlying mechanisms are improved by this integrative approach. In this study, hybrid modelling is employed using conventional statistical methodologies as well as the most recent machine learning technique. Logistic Regression, Neural network and Cox Proportional Hazards have been used widely in medical research as they are easy to interpret and can reasonably handle specific types of data. Nevertheless, they are unable to deal well with high dimensions, complex datasets (Belsti et al., 2023), like the genomic ones. Random Forest, Support Vector Machine (SVM) and Gradient Boosting Machine (GBM) are appropriate for dealing with large datasets and recognizing subtle patterns. The study attempts to integrate these approaches to develop highly accurate, yet interpretable, predictive models (Boateng et al., 2023). The sheer volume and complexity of genomic data present significant challenges in analysis and interpretation. In addressing these problems, this study provides hybrid models to maximize predictive power of cervical cancer outcomes and subsequently improves patient care and personalized treatment strategies.

Accurately predicting health outcomes is significant in medical research in the age of precision medicine (Ahmadzia et al., 2024). Combining statistical methods and the machine learning algorithms in the hybrid models, this study aims to improve the predictive reliability of cervical cancer outcome predictions. The study then allows for a comprehensive analysis of health outcomes by considering both genetic and environmental factors on the data provided by patient clinical data and genomic information (Zhuang et al., 2024). The accuracy of the prediction and its underlying mechanisms are improved by this integrative approach. In this study, hybrid modelling is employed using conventional statistical methodologies as well as the most recent machine learning technique. Logistic Regression, Neural network and Cox Proportional Hazards have been used widely in medical research as they are easy to interpret and can reasonably handle specific types of data. Nevertheless, they are unable to deal well with high dimensions, complex datasets (Belsti et al., 2023), like the genomic ones. Random Forest, Support Vector Machine (SVM) and Gradient Boosting Machine (GBM) are appropriate for dealing with large datasets and recognizing subtle patterns. The study attempts to integrate these approaches to develop highly accurate, yet interpretable, predictive models (Boateng et al., 2023). The sheer volume and complexity of genomic data present significant challenges in analysis and interpretation. In addressing these problems, this study provides hybrid models to maximize predictive power of cervical cancer outcomes and subsequently improves patient care and personalized treatment strategies.