Towards combining self-organizing maps (SOM) and convolutional neural network (CNN) for improving model accuracy: Application to malaria vectors phenotypic resistance

Abstract

This study introduces a hybrid approach that combines unsupervised self-organizing maps (SOM) with a supervised convolutional neural network (CNN) to enhance model accuracy in vector-borne disease modeling. We applied this method to predict insecticide resistance (IR) status in key malaria vectors across Africa. Our results show that the combined SOM/CNN approach is more robust than a standalone CNN model, achieving higher overall accuracy and Kappa scores among others. This confirms the potential of the SOM/CNN hybrid as an effective and reliable tool for improving model accuracy in public health applications.

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  The hybrid model, combining SOM and CNN, was implemented to predict IR status in malaria vectors, providing enhanced accuracy across various validation metrics.
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  Results indicate a notable improvement in robustness and predictive accuracy over traditional CNN models.
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  The combined SOM/CNN approach demonstrated higher Kappa scores and overall model accuracy.