Publication Type
Journal Article
Journal Name
MethodsX
Publication Date
6-1-2025
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. • 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.
Keywords
Data science, Decision making, Insecticide resistance, Unsupervised and supervised learning
Recommended Citation
Agboka, K., Abdel-Rahman, E., Salifu, D., Kanji, B., Ndjomatchoua, F., Guimapi, R., Ekesi, S., & Tobias, L. (2025). Towards combining self-organizing maps (SOM) and convolutional neural network (CNN) for improving model accuracy: Application to malaria vectors phenotypic resistance. MethodsX, 14 https://doi.org/10.1016/j.mex.2025.103198