ABSTRACT
Cervical cancer is one of the leading causes of cancer-related deaths among women worldwide. Early detection plays a crucial role in improving survival rates. This study explores the application of deep learning techniques, particularly Convolutional Neural Networks (CNNs), for the classification of cervical cancer based on clinical and demographic features. In addition to traditional machine learning models such as XGBoost, Support Vector Machine (SVM), and Random Forest, we employ CNNs to capture complex patterns in the data and enhance classification accuracy. The dataset is preprocessed using feature selection and data normalization techniques to optimize model performance. A comparative analysis is conducted using performance metrics such as accuracy, precision, recall, and F1-score. The results demonstrate that CNN-based deep learning models outperform conventional machine learning approaches, highlighting their potential for accurate and early cervical cancer detection, leading to timely medical intervention.
Keywords: Cervical cancer, deep learning, Convolutional Neural Networks (CNN), classification, XGBoost, Support Vector Machine (SVM), Random Forest, early detection, feature selection, predictive modeling.

OBJECTIVES
1. To develop a deep learning-based model using Convolutional Neural Networks (CNNs) for the classification of cervical cancer using clinical and demographic data.
2. To preprocess and analyze the dataset using feature selection and normalization techniques to enhance model performance.
3. To train and evaluate the CNN model using performance metrics such as accuracy, precision, recall, and F1-score.
4. To validate the CNN model on unseen test data to ensure its reliability and generalizability.
5. To develop an efficient AI-driven approach that aids in the early detection and diagnosis of cervical cancer, improving accessibility to timely medical intervention.