Empirical Evaluation of Deep Learning Approaches for Predicting Cervical Cancer in the Health Care Sector

This research paper addresses the urgent need to combat the escalating mortality rates in cervical cancer, impacting 570,000 women, with 311,000 fatalities, as reported by the World Health Organization. Recognizing the potential of digital solutions, we explore deep learning’s untapped power for early diagnosis. Amidst healthcare challenges due to population growth and disease spread, traditional methods prove inadequate. To bridge this gap, we introduce novel techniques: Long Short-term Memory Networks and Bidirectional Long Short-term Memory Networks. Leveraging a comprehensive dataset of 15 attributes, including age, pregnancies, partners, smoking, cytology, and biopsy, our model achieves a noteworthy 97% accuracy, signifying a ground-breaking advancement in cervical cancer management.