Applied Study for Predicting Lung Cancer Patients Using Box and Jenkins Systems at Misurata Center for Oncology - Bin Jabir, Wafa Alhusayn; Aldofani, Samira Omar; Alnakwa, Aishah Abd Alslam (The annual Scientific Conference for Under and Postgraduate Students at the University., 2024)
Future forecasting is one of the main topics that enable decision makers to make sound decisions in the health, social, service and other fields. Time series are considered among the most important modern statistical methods through which one can know the nature of the changes that occur in the values ​​of the phenomenon over time and determine the causes and results. And interpreting the relationships observed between them and predicting the changes that will occur in the values ​​of the phenomenon in the future in light of what happened to it in the past. The problem of the research lies in determining an appropriate model to predict the numbers of people infected with lung cancer in the Misurata Oncology Center Which is considered a serious disease that affects the respiratory system of many people in this era, using the Box and Jenkins method of time series analysis, so that the center’s administrators can develop plans and measures and take the necessary precautions. The research aims to introduce the Box and Jenkins method in time series analysis based on data for lung cancer patients taken from the Misurata Oncology Center during the period (2004-2032) and to predict for the next ten years, as well as knowing the incidence of lung cancer in males and females, and the results of the research reached The series of people with lung cancer is an unstable series and has a general trend toward increase, and the appropriate model for it is ARIMA (0,1,1), according to diagnostic tests. The results of the study also found that the model ARIMA (0,1,1). It is the most efficient of the three proposed models according to statistical comparison criteria (mean absolute error, mean relative error, Bayesian index), and it predicts the number of lung cancer patients in the year 2032.