Towards combining self-organizing maps (SOM) and convolutional neural network (CNN) for improving model accuracy:...

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