<![CDATA[This study explores the impact of Artificial Intelligence (AI) implementation in the integrated prediction of tuberculosis (TB) and anemia spread. The primary aim is to assess how AI technologies, such as machine learning algorithms and predictive modeling, can enhance the accuracy and efficiency of forecasting TB and anemia prevalence in different populations. The research employs a combination of data from healthcare databases, epidemiological studies, and patient records, analyzed using AI-driven tools to identify patterns, correlations, and predictive factors for the spread of these diseases. Results show that AI significantly improves the predictive capabilities, offering more precise and early identification of areas at risk, thus aiding healthcare providers in deploying targeted interventions. The integration of TB and anemia prediction using AI also allows for more effective resource allocation, early diagnosis, and improved patient outcomes. This study highlights the importance of AI in transforming healthcare practices and disease control efforts, suggesting that the integration of AI technologies could lead to more proactive public health strategies. The findings contribute to the growing body of knowledge on the intersection of AI and epidemiology, advocating for further research and wider adoption of AI-driven solutions in global health initiatives.]]>
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