Abstract:
Cervical cancer is a leading cause for women and is threatening women's health globally and it is difficult to observe any signs in the very early stage. But Machine learning will be a good option for doctors to classify and identify the cancer of medical patients. Our aim is to help doctors by identifying and classifying the cancer of the patients at its early stage. Here, machine learning will be a good option for doctors to classify and identify the cancer of medical patients. Our aim is to help doctors by identifying and classifying the cancer of the patients at its early stage. We use the "Cervical Cancer Behavior Risk Dataset"in this experiment. The dataset was imbalanced so we applied some data processing techniques to make the dataset balanced for this purpose, we used some techniques such as removing null values, dropping unnecessary columns, and using a label encoder. we also used six models to analyze the cervical cancer dataset using Random Forest, Decision Tree, Logistic Regression, Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), and K-Nearest Neighbor Classifier to predict cervical cancer from behavior, and we got maximum 97.76% accuracy. Moreover, machine learning plays a major role in cancer classification and identification in saving human life.
