PENERAPAN PRINCIPAL COMPONENT ANALYSIS (PCA) DALAM PENENTUAN FAKTOR DOMINAN YANG MEMPENGARUHI PENGIDAP KANKER SERVIKS (Studi Kasus : Cervical Cancer Dataset) - Muhammad Zulfahmi Nasution (Institute of Computer Science (IOCS), 2019)
Cancer diagnosis is a frightening issue for patients and can affect the patient's psychological condition. Therefore, by giving attention and psychosocial support to cancer patients it is expected to overcome the psychological pressure of the patient. Screening is an early detection effort to identify a disease or disorder that is clinically unclear by using certain tests, examinations or procedures. This effort can be used quickly to distinguish people who seem healthy but actually suffer from an abnormality. The purpose of this study is to simplify and eliminate some less relevant screening without reducing the intent and purpose of the original data using the Principal Component Analysis (PCA). Based on the results of research conducted from the UCI Cervical Cancer dataset repository, shows that there are 9 dominant screening variables that have a large enough correlation to the formation of early detection of cervical cancer with a proportion of 99% of covariance variance, including the 3 highest factors very dominant, namely the age factor with the first highest eigenvalue that is 76.05 with the proportion of variance 58.50%, cigarette addicts with 14.90% variance and Hormonal Contraceptives factor with 9.3% variance. The total variance obtained from the 9 screening variables is 99%. Keywords: Cervical Cancer, Screening, Principal Component Analysis, Eigenvalue, Factor Analysis