A new approach for diabetes risk detection using quadratic interpolation flower pollination neural network

A new approach for diabetes risk detection using quadratic interpolation flower pollination neural network

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...

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Bibliographic Details
Main Authors: Yulianto Triwahyuadi POLLY, Adriana FANGGIDAE, Juan Rizky Mannuel LEDOH, Clarissa Elfira AMOS PAH, Bertha S. DJAHI, Kisan Emiliano Rape TUPEN
Format: Article
Language:English
Published: Polish Association for Knowledge Promotion 2025-06-01
Series:Applied Computer Science
Subjects:
Diabetes detection
QIFPNN
accuracy
F1-score
Online Access:https://ph.pollub.pl/index.php/acs/article/view/7186
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