This study aims to evaluate and compare five algorithms in diabetes detection, namely Flower Pollination Neural Network (FPNN), Particle Swarm Optimization Neural Network (PSONN), Bat Artificial Neural Network (BANN), Stochastic Gradient Descent (SGD), and Quadratic Interpolation Flower Pollination Neural Network (QIFPNN). These algorithms were tested on a diabetes risk dataset divided into training, validation, and testing subsets. The evaluation was based on three main aspects: accuracy, F1 score, and training time. Experimental results showed that QIFPNN outperformed others with an average accuracy of 97.90% and an F1 score of 98.30%, although it required the longest training time (4107.89 seconds). FPNN and BANN achieved competitive accuracy (97.34% and 97.43%) and F1 scores (97.84% and 97.91%), while SGD offered a favorable trade-off with accuracy of 96.87%, F1 score of 97.42%, and the shortest training time (584.50 seconds). PSONN performed less well with an average accuracy of 89.26% and an F1 score of 91.45%. These results indicate that QIFPNN can be relied upon as an effective diabetes risk detection model with superior predictive performance. Although the training time of QIFPNN is longer due to its sophisticated optimization process, this is only a concern during model development, as the final trained model can be efficiently used for real-time prediction in practical applications.

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