An Overview of Integrative Approaches Combining ADE and LASSO for Feature Selection in Ovarian Cancer Detection
B. Vijaya Kumari, Vishwanath Bijalwan · 2025
The inconspicuous signs and intricate molecular characteristics of ovarian cancer make it difficult to detect it in its early stages. For efficient feature selection in ovarian cancer diagnosis, this research utilizes Adaptive Elastic Nets (ADE) and Least Absolute Shrinkage and Selection Operators (LASSO). To ensure superior quality input for analysis, extensive data collection including clinical, demographic, and genetic characteristics is biomarkers associated with ovarian cancer and improve predictive precision, thereby improving diagnostic accuracy. In addition to providing valuable insights into ovarian cancer's molecular underpinnings, this integrative methodology lays the groundwork for future research into customized therapies.