Abstract
This study presents a comparative time series analysis of annual cancer incidence in Nigeria from 2015 to 2024, exploring the predictive capacity of artificial intelligence (AI) versus observed secondary data. Leveraging AI-generated projections and statistical trend modelling, we estimate an annual increase of approximately 107,000 new cancer cases, with prostate, cervical, and breast cancers being the most prevalent. The AI model revealed a trend equation T=107198.47+1064.0btT = 107198.47 + 1064.0btT=107198.47+1064.0bt, while the Durbin-Watson test returned a statistic of d=3.63d = 3.63d=3.63, indicating negative autocorrelation in the dataset. Secondary data retrieved from scientific repositories and national health records were juxtaposed with AI outputs to evaluate deviations and model accuracy. The results underscore the potential of AI in forecasting disease patterns and highlight discrepancies due to underreporting and data lags in traditional datasets. By integrating AI into national health surveillance systems, policymakers can proactively allocate resources, optimize interventions, and enhance early detection strategies. This interdisciplinary approach aligns with the conference’s focus on operational efficiency, strategic planning, and institutional capacity building using AI innovations.
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