Strategic planning for cancer control: Utilizing machine-learning models to predict future incidences

Abstract

One of the top health issues in the world is the Cancer. The number of patients with various types of cancer registered in clinics, oncology centers and hospitals are annually increasing. Predicting the estimated number of future cancer incidences for subsequent years is an important topic and needs further study. In this paper, officially registered cases at the Ministry of Health, Iraq and Oncology Center at Kirkuk governorate are used to forecast three years ahead estimated number of incidences for different types of cancer using four machine-learning models. The considered models are; gaussian processes, multilayer perceptron (MLP), MLP regressor, and sequential minimal optimization regressor (SMOreg). The study reveals significant differences among different models based on performance metrics. Based on SMOreg analysis in 2023, most types of cancer are expected to see an increase in cases. For instance, bladder cancer is projected to rise from 13 cases in 2020 to around 33 cases in 2023, a 153.8% increase. Results show that, the prediction of incidence cases using SMOreg model is outperformed other algorithms with minimized error in most types of cancer. According to the results, there will be a rise in the number of incidences in the 2023 for most types of cancer except breast and non-hodgkin lymphoma cancer, which expected a decrease in the number of cases. By utilizing of three-year-ahead annual numbers of cancer cases predictions, governments will be able to hedge financial risks, provide patients care and plan for cancer control programs.