Determination of Dominant Factors Affecting Lung Cancer Patients Using Principal Component Analysis (PCA)

Abstract

The diagnosis of lung cancer is one of the most pressing health issues as the disease is often only detected at an advanced stage, leading to a poor prognosis for patients. Therefore, in an effort to detect, prevent, and manage the disease more effectively, this study utilized screening variables. Screening is an important endeavor in the early detection of diseases or abnormalities that are not yet clinically apparent using various tests, examinations, or procedures. The use of screening variables is very important in the early detection process because it can help in this study to understand the risk factors and causes of disease. The purpose of this study is to determine the dominant factors affecting people with lung cancer using Principal Component Analysis (PCA). Based on the results of the study, it was found that there are 20 dominant screening variables that have a considerable correlation to the formation of early detection of lung cancer with a total proportion of covariance variance of 100% when, the total variance obtained from the 20 screening variables is 100%. The final PCA results show that the factor loading values are used to determine which variables are most influential by comparing the magnitude of the correlation. As a result, the main factor causing lung cancer was Fatigue which had a factor loading of 7.87%, followed by the variables Age and Alcohol use with a factor loading of 6.02%. Other variables also showed certain factor loadings that indicated the cause of lung cancer. These findings are very important in efforts to improve early detection and management of lung cancer through more effective and targeted screening.