Email this article Machine Learning-Based Analysis of Cancer Incidence in Jordan (2020–2021): A Decision Tree Approach
Abstract
This study aimed to predict cancer types across different regions of Jordan using a machine learning-based Decision Tree Algorithm (DTA) model. The model employed patients’ demographic information—specifically gender, age, and region of residence—as independent variables (IVs) to assess their interaction with cancer types as the dependent variable (DV). The objective was to determine the predictive relationship between these demographic factors and cancer types to support regional cancer profiling and inform targeted public health planning. The research utilized secondary data from the Ministry of Health, the Directorate of Non-Communicable Diseases, and the Jordan Cancer Registry for the years 2020 to 2021. A total of 9,547 cancer cases were analyzed using the DTA model, which effectively identified significant incidence patterns, with the central region of Jordan accounting for the highest number of cases (n = 6,815; 71.4%). The model classified cancer into 25 distinct types based on demographic attributes, with breast cancer being the most prevalent, particularly among middle-aged females residing in the central region. The DTA model demonstrated high efficacy in handling and stratifying large-scale medical data, predicting cancer type interactions, categorizing and labeling datasets, and suggesting potential category mergers. These findings have important implications for the development of focused cancer prevention strategies and the efficient allocation of healthcare resources. However, a key limitation of the study is the incomplete characterization of cancer patient attributes across all Jordanian regions.
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