Waikato Environment for Knowledge Analysis (WEKA) as a Data Analysis Method Identifying Potential Hematological Parameters for Early Diagnosis of Cervical Cancer

The consistency in conclusions drawn from data mining and statistical analyses highlight the utility of hematological parameters as potential non-invasive biomarkers for cervical cancer screening and progression monitoring. These findings suggest that integrating machine-learning algorithms, particularly random forest, with hematological analysis might enhance early diagnosis and improve clinical outcomes for patients with cervical abnormalities.