Leveraging Deep Learning to Analyze Patient Medical Records for Cervical Cancer Prediction
N. Indumathi, D. Sorna Shanthi · 2025
Deep learning (DL) is being used by more and more people and organizations to evaluate massive datasets and derive insightful information. Advanced DL algorithms are now frequently used in the healthcare industry to forecast the early start of serious illnesses like cancer, heart disease, and renal failure. Due mostly to delayed detection, cervical cancer continues to be a major worldwide health concern and one of the leading causes of mortality for women. Better survival rates and efficient treatment depend on early detection. This paper proposes a deep learning method based on medical records that uses Bi-LSTM to predict the risk of cervical cancer. Data preprocessing, feature selection, model training, and evaluation are all part of the methodology. When compared to more conventional techniques like logistic regression and SVM, the suggested model yields an AUC-ROC score of 0.93. Additionally, by emphasizing important risk factors, SHAP improves interpretability. To support the effectiveness of the model, a detailed explanation of the confusion matrix and performance indicators is provided. The applicability of LSTM and BERT approaches to sequential data processing is briefly examined. To classify risk levels, the proposed approach entails pre-processing patient records, extracting key features, and training a deep learning model. The model's ability to support medical practitioners by improving diagnostic accuracy is confirmed by experimental results. Additionally, AI-driven technology enhances conventional diagnostic techniques by lowering the strain on medical staff and providing individualized insights for every patient. By incorporating deep learning into cervical cancer screening, early detection rates might rise, healthcare resource allocation could be optimized, and mortality could eventually decline.