A Machine Learning-Based Epidemiological Analysis of Cancer Distribution in Yemen
DOI:
https://doi.org/10.65693/j21umas.2026.v5i1.370الكلمات المفتاحية:
Cancer epidemiology; Yemen; Machine learning; Random Forest; Cancer prevalence; Geographical distribution.الملخص
Cancer represents a severe public health crisis in Yemen. Current literature lacks the regional granularity required for effective policy. To bridge this gap, the authors parsed 5,226 clinical records from the National Oncology Centre, mapping the spatial and demographic dispersion of the disease across all governorates. After scrubbing raw data for inconsistencies, the authors deployed Random Forest classification and hierarchical clustering to quantify patient demographics, temporal shifts, and regional incidence rates. Cases cluster heavily in specific regions. Ibb, Taiz, and Dhamar alone account for over one-third of the total national caseload. Breast cancer is the primary diagnosis. Most patients fall within the middle-adulthood cohort. Hierarchical clustering partitioned the governorates into subgroups with shared oncological profiles, providing a framework for localized intervention. The machine learning models yielded a Top-1 accuracy of 0.192 and a Top-3 accuracy of 0.596. These metrics reflect the high diagnostic heterogeneity and the asymmetric distribution of cases inherent to the Yemeni landscape. This work establishes an empirical baseline for national cancer planning. These results enable evidence-based resource management and prevention programs tailored to regional epidemiological realities.
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الحقوق الفكرية (c) 2026 Abdulrahman M. H. Obaid، Gameil Ali، Yousif Alhaj، Awadh Ali Abdo Mohammed Awadh Ali Abdo Mohammed (المؤلف)
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