MODEL SURVIVAL HIDUP PASIEN KANKER MATA MENGGUNAKAN METODE KAPLAN-MEIER DAN REGRESI COX PROPORTIONAL HAZARD - Deyana Maulidya Rahmawan; Delia Nur Haliza; Samsul Arifin; Al Hujjah Asianingrum (EPSILON: JURNAL MATEMATIKA MURNI DAN TERAPAN, 2026)
Eye cancer is a relatively rare disease, but it can cause permanent vision impairment and even death if not treated properly. Differences in cancer type and patients’ clinical conditions are suspected to play a role in determining survival, so survival analysis is needed to describe survival patterns and identify factors affecting the risk of death. This study aims to analyze the survival of eye cancer patients and identify clinical factors influencing mortality risk using the Kaplan–Meier method and Cox Proportional Hazard regression. The research data were obtained from the Eye Cancer Patient Records dataset on the Kaggle platform, consisting of 5,000 medical records of eye cancer patients. Of these, 350 observations were used for Kaplan–Meier curve visualization so that survival patterns between groups could be more easily interpreted, while the Cox regression analysis was conducted using the prepared research data according to the modeling requirements. The results showed that cancer type was the only factor that significantly affected the risk of death. Patients with intraocular lymphoma had a hazard ratio of 1.54, meaning they had a 1.54 times higher risk of death compared to patients with retinoblastoma. Meanwhile, other variables such as gender, stage at first diagnosis, treatment type, surgery status, radiation therapy, and chemotherapy did not show a significant effect. Overall, these findings indicate that cancer type is the most prominent factor distinguishing survival between the two eye cancer groups analyzed.