INTRODUCTION: Cervical cancer is a significant public health concern worldwide, with early detection being critical for successful treatment outcomes. In this project, I utilized deep learning techniques employing Keras with TensorFlow backend to analyze a cervical cancer dataset. The goal was to extract insights and develop a predictive model to aid in the early diagnosis of cervical cancer.

INSIGHTS: -Does age influence the likelihood of developing cervical cancer? -Analyzed the distribution of cervical cancer cases across different age groups. -Determined if there's a correlation between age and the presence of cervical cancer. -Is there a gender-based predisposition to cervical cancer? -Investigated the proportion of cervical cancer cases among different genders. -Explored whether gender plays a significant role in cervical cancer diagnosis. -How does sexual activity impact cervical cancer risk? -Examined the relationship between sexual activity and cervical cancer incidence. -Assessed if certain sexual behaviors or practices contribute to a higher risk of cervical cancer. -Can the number of pregnancies be a predictor of cervical cancer? -Investigated the association between the number of pregnancies and cervical cancer diagnosis. -Determined if multiparity is linked to an increased likelihood of cervical cancer. -What other demographic factors are correlated with cervical cancer? -Explored additional demographic variables such as race, ethnicity, socioeconomic status, and geographical location. -Identified any demographic patterns or disparities in cervical cancer prevalence. -How do cytology test results relate to cervical cancer prediction? -Analyzed the distribution of cytology test outcomes (e.g., Pap smear results) among cervical cancer cases and non-cancer cases. -Evaluated the predictive power of cytology test results in identifying cervical cancer early. -Can a combination of demographic and clinical features improve predictive accuracy? -Investigated the synergistic effects of multiple features (e.g., age, gender, sexual activity, cytology results) on cervical cancer prediction. -Built predictive models incorporating various features to enhance early detection capabilities.