Comparative study of machine learning and statistical survival models for enhancing cervical cancer prognosis and risk factor assessment using SEER data.

Cervical cancer is a common malignant tumor of the female reproductive system and the leading cause of death among women worldwide. The survival prediction method can be used to effectively analyze the time to event, which is essential in any clinical study. This study aims to bridge the gap between traditional statistical methods and machine learning in survival analysis by revealing which techniques are most effective in predicting survival, with a particular emphasis on improving prediction accuracy and identifying key risk factors for cervical cancer.

Assessment of factors related to poly cystic ovarian syndrome - A comparative and correlational study.

Polycystic ovarian syndrome (PCOS) is a common endocrine disorder that primarily affects women of reproductive age. It is particularly prevalent among adolescent females who receive an insufficient diagnosis despite having potentially adverse consequences. The use of PCOS screening questionnaires has the potential to aid in the early detection of symptoms.