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Predicting the spread of cholera disease in different geographical areas - case study of Lagos state using a random forest model with spatial-temporal analysis

 Department: Computer Science  
 By: usericon MIke_Tech_World  

 Project ID: 9716
   Rating:  (5.0) votes: 1
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   Price:₦5000
Abstract
This project focuses on predicting the spread of cholera in various geographical areas within Lagos State, utilizing a Random Forest model combined with spatiotemporal analysis. Cholera remains a significant public health challenge in densely populated regions like Lagos, where inadequate sanitation and environmental factors exacerbate outbreaks. By integrating epidemiological, environmental, and socio-economic data, the model aims to forecast cholera occurrences more accurately, enabling timely interventions. Data collection involved historical cholera records, environmental variables such as rainfall and temperature, and spatial data for geographical analysis. After extensive data preprocessing and feature engineering, the model was trained using Random Forest, which was selected for its robustness in handling complex, high-dimensional datasets. The model''s performance was evaluated using Mean Absolute Error (MAE) and R² score, achieving an MAE of 41.34 and an R² score of -0.11, indicating areas for further optimization. The analysis revealed significant spatial and temporal patterns in cholera outbreaks, highlighting high-risk areas consistent with historical data. Seasonal trends were also identified, with cholera cases peaking during the rainy season due to increased water contamination. Visualizations generated from the spatial analysis provided insights into potential hotspots, which can aid public health officials in resource allocation and preventive measures....
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