Automated Cervical Cancer Prediction using Ensemble Machine Learning based Approaches

Cervical cancer is a widespread type of cancer that primarily affects women. Detecting it early is crucial for successful treatment and recovery. However, detecting cervical cancer before it spreads or reaches an advanced stage poses a significant challenge for the medical industry. To address this issue, a study was conducted that utilized various machine-learning classification models to predict cervical cancer. The dataset was balanced using the Adaptive Synthetic Sampling approach, and principal component analysis was used to select the top 13 features. Performance was evaluated based on several metrics, including Accuracy Score, Precision Score, Recall Score, and F1 Score. The research aimed to enhance the existing model and provide a more efficient one. The study's results are discussed in detail in this article, and medical institutes can leverage these methods to predict cervical carcinoma.