Data Reliability and Survival Analysis for Breast Cancer Patients Using Real Data

Among the illnesses that have recently been identified as endangering the lives of females globally is breast carcinoma. Early detection of breast cancer is crucial since it increases the range of available treatments and raises patients' chances of surviving. This study aimed to examine data reliability. This study, conducted on 760 female patients, was a comprehensive investigation into the reliability of breast cancer data. The data was self-gathered from a healthcare hospital from October 2019 to October 2022, ensuring a complete and accurate representation. Mammography reports and personal information were collected. Initially, a KMO (Kaiser-Meyer-Olkin) test was carried out to ensure the data size was appropriate for exploratory factor analysis. The score must be greater than 0.80 for acceptance. The assessment of the KMO test concerning the collected dataset is 0.86, which is higher than the threshold, demonstrating the thoroughness of data collection. Then, Bartlett's test of sphericity (P is 0) proved that the sample size for exploratory factor analysis was adequate and that the normal distribution was present. Furthermore, the reliability was evaluated employing Cronbach's alpha coefficient. The Kaplan-Meier test has been utilized to assess the consequences of various health parameters on cancer patients' survival over time.