Hybrid Adaboost – Catboost Model for Prognostic Analysis of Uterine and Cervical Cancer
DOI:
https://doi.org/10.15662/IJEETR.2026.0802056Keywords:
Cancer prognosis, Hybrid learning, AdaBoost, CatBoost, Machine learning, Uterine cancerAbstract
Cancer prognosis is really important for diagnosing cancer and planning the right treatment especially for uterine and cervical cancer, which are big causes of death for women all around the world. This paper is about a way of using machine learning to predict what will happen with uterine and cervical cancer by combining different methods to make predictions more accurate and reliable. The model we made combines two techniques, called AdaBoost and CatBoost to predict what grade a tumor is and whether a patient will survive. We used a lot of information from patients, including diagnostic features and we cleaned and organized the data to make our model work better.
We tested our model using some metrics, like accuracy, precision, recall, F1-score and ROC-AUC. What we found out is that our hybrid model is better than some models like Logistic Regression and Random Forest when it comes to predicting what will happen.The results show that using learning models can really improve our ability to predict what will happen with cancer, which can help doctors make better decisions. This work shows how machine learning can be used to help diagnose cancer. It gives us a framework to keep doing research, on cancer prognosis systems.The new hybrid model is really good at what it does. It works better than the machine learning ways. This makes the hybrid model a great choice for systems that help doctors figure out what is going on with cancer. The hybrid model is very helpful, for people who want to know what will happen with their cancer.
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