Improving Predictions of Infectious Disease Outbreaks Using Machine Learning
Author Information
Author(s): Abdallah Reham, Abdelgaber Sayed, Sayed Hanan Ali
Primary Institution: Helwan University, Cairo, Egypt
Hypothesis
Can advanced machine learning techniques improve the prediction of infectious disease outbreaks?
Conclusion
The study demonstrates that an ensemble model achieves the highest accuracy for predicting Zika outbreaks at 96.80% and effectively balances precision and recall for Chikungunya.
Supporting Evidence
- The ensemble model achieved the highest accuracy rate of 96.80% for predicting Zika outbreaks.
- The model exhibited consistent performance across various metrics.
- In the context of Chikungunya, the ensemble model achieved an accuracy of 93.31%, with a precision of 57% and a recall of 63%.
- The study utilized a comprehensive dataset comprising climate and socioeconomic data from 2007 to 2017.
Takeaway
This study shows how computers can help predict when diseases like Zika and Chikungunya might spread, which can help keep people safe.
Methodology
The study used machine learning algorithms, including Random Forest, XGBoost, Gradient Boosting, and an ensemble model, along with the Analytic Hierarchy Process for feature selection.
Potential Biases
The study's reliance on historical data may introduce biases related to data quality and availability.
Limitations
The study faced limitations related to data availability, particularly for Chikungunya and Zika, and lacked comprehensive clinical data at the patient level.
Statistical Information
P-Value
p<0.05
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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