Forecasting the incidence rates of top three cancers in Malaysia

• 出版年: AIP Conference Proceedings
• 第一著者: 2-s2.0-85148912350
• フォーマット: Conference paper
• 言語: English
• 出版事項: American Institute of Physics Inc. 2023
• オンライン･アクセス: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85148912350&doi=10.1063%2f5.0110929&partnerID=40&md5=f62a3fcdd23d1f0611097fdb0b1034f2

Cancer incidence is defined as the number of new cases of cancer in a specific population for a specific period. In Malaysia, the incidence rates for cancer keep increasing each year especially the top three cancers namely female breast, colorectal and lung cancer. The increasing trend has impacted the society economically and psychosocially. The cost of treating cancer and the efforts to psychologically manage the emotions of the patients are crucial as the patient's financial, social and physiological situations may all change accordingly. Having an informed estimations about future life with cancer is helpful in preparing for better lives. Through earlier indication of cancer predictions, effective resource planning and attentive mental wellbeing of future healthcare frameworks can be well anticipated. Therefore, having the right model to accurately forecast the incidence rates is deemed essential. This study aims to determine the best model between Box-Jenkins ARIMA and Exponential Smoothing in forecasting the incidence rates. The model with the least Mean Absolute Percentage Errors (MAPE) value, is determined as the best model and used to forecast the top three cancer incidences for the year 2017 to 2021. This forecast period is in accordance to the periodical report of Malaysia National Cancer Registry (MNCR) published by the Ministry of Health (MOH), Malaysia which is yet to be published. Hence, these forecast figures will backtest the upcoming MNCR 2017-2021 report. Results show that the Exponential Smoothing model predominantly outperformed the ARIMA model. Only for Female Colorectal Cancer, ARIMA model performs better than Exponential Smoothing. © 2023 Author(s).