Parameters Evaluation of Flower Pollination Neural Network for Malaria and Dengue Detection

Abstract

As the capital city of East Nusa Tenggara Province (NTT), Kupang City faces serious issues related to waste management. The accumulation of unmanaged waste leads to water stagnation and creates ideal breeding grounds for mosquitoes, thereby increasing the risk of disease transmission such as Dengue Fever (DBD) and Malaria. Individuals aged 0–20 years and females tend to have higher rates of DBD, while malaria most commonly affects young children who have not yet developed immunity and pregnant women whose immunity is reduced due to pregnancy. To address this challenge, this study proposes a novel approach: the Flower Pollination Neural Network (FPNN) for classifying DBD and malaria cases. FPNN is a hybrid model combining Neural Networks (NN) optimized using the Flower Pollination Algorithm (FPA). FPA is a metaheuristic algorithm that often employs stochastic search. It is capable of optimizing the weights of NN, avoiding local optima traps, and minimizing errors in NN. This study uses 10-fold cross-validation to evaluate FPNN’s ability to generalize to unseen data. The best results were achieved using a parameter combination of switch probability 0.7, 15 neurons in the hidden layer, and a population size of 25, yielding an accuracy of 86.4% and an F1-score of 72.312%. Although this parameter combination does not outperform all others in terms of accuracy, it consistently delivers good accuracy and demonstrates strong data generalization capabilities. Furthermore, it achieved the highest F1-score, indicating a good balance between pattern recognition during training and prediction performance on unseen data.