Cervical cancer is a primary cause of death in women. Human Papilloma Virus (HPV) virus is the main factor that causes cervical cancer. Therefore, risk reduction of cervical cancer is cervical cancer screening and vaccination against infection of HPV. The methods of cervical cancer screening are medical history, HPV virus detection, Pap smear detection, and small pieces of meat cutting. However, Pap smear detection is a popular technique for cervical cancer screening. In addition, the classification method is used to increase the performance of cervical cancer detection. Therefore, our research proposed method of cervical cancer detection using hybrid classification methods with risk factor data of cervical cancer and cervical cancer behavior risk data, which is divided into two stages: primary risk estimation of a chance cervical cancer and cervical cancer prediction. Firstly, the dataset was prepared to handle missing values and imbalanced data. Secondly, we select suitable attributes for model building. Finally, the dataset was used to build the model using hybrid classification methods by bagging with Random Forest. From our experiment, the model for primary risk estimation of a chance cervical cancer and cervical cancer prediction has an accuracy of 99.25% and 98.00%, respectively. The result shows that our proposed method accurately predicts cervical cancer compared to state-of-the-art methods. Therefore, it confirms that our proposed model can be applied to use for primary risk self-estimation of a chance of cervical cancer before deciding to meet a physician. Moreover, it helps the physician with diagnosis and treatment to reduce costs and the death rate.

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