An Efficient and Hybrid Antlion Optimization-Based Genetic Algorithm Assisted Machine Learning Technique: A Metaheuristic Approach for Dengue Prediction
DOI:
https://doi.org/10.52783/jns.v14.2024Keywords:
Optimization, feature selection, dengue fever, over-sampling, synthetic sampling, accuracyAbstract
Dengue Prediction is a very valuable task in public health, particularly in areas that are most at risk of dengue fever. In this research, an improved the Antlion Optimization Algorithm (ALO) with a Genetic Algorithm (GA) are proposed and applied to improve the predictive accuracy of dengue cases. The precondition for input data preparatory is an elaborate pre-processing pipeline which include handling missing values using mode imputation, removing noise using outlier detection methods and normalizing data. Categorical variables are then encoded While class imbalance problems are addressed either by Synthetic Minority Over-sampling Technique (SMOTE). After that feature selection and extraction processes are fine-tuned using the hybrid ALO-GA approach; adopting ALO for feature selection and GA for tuning the parameters, the proposed technique minimizes overfitting and improve the accuracy of the classifier. The proposed hybrid model is compared with other classifiers under the machine learning domain and good performance improvements are found in areas such as sensitivity, specificity, and F1-score. This metaheuristic approach allows for choosing the right epidemiological and environmental variables for the model making it more accurate.
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