OptiANN-LR: Augmenting Diabetes Prediction Accuracy through Hyper Learning Rate Tuning in Optimized Artificial Neural Networks

Diabetes, a pervasive chronic condition, presents global health challenges. We propose a novel approach to address this, optimizing Artificial Neural Networks (ANNs) through hyperparameter tuning. Our model, OptiANN-LR, achieves an exceptional 95.57% accuracy, surpassing ABP-SCGNN 93%. Success hinge...

Full description

Saved in:
Bibliographic Details
Main Authors: P, Sujitha S, Anita, C. S., Sudharson, K., Rose, S. Remya, S, Sharmila, V, Keerthana
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Diabetes
diabetes prediction
early diagnosis
hyperparameter tuning
Learning (artificial intelligence)
machine learning in healthcare
Medical services
Parallel processing
Software
Training
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Diabetes, a pervasive chronic condition, presents global health challenges. We propose a novel approach to address this, optimizing Artificial Neural Networks (ANNs) through hyperparameter tuning. Our model, OptiANN-LR, achieves an exceptional 95.57% accuracy, surpassing ABP-SCGNN 93%. Success hinges on meticulous learning rate tuning, a crucial ANN hyperparameter. With the Adam optimizer, OptiANN-LR efficiently converges, and dropout layers curb overfitting, ensuring robust generalization. These enhancements promise early diabetes diagnosis and prevention. Rigorous evaluations employing k-fold cross-validation and diverse metrics affirm OptiANN-LR's reliability. Our work showcases advanced machine learning in healthcare, especially diabetes prediction, advancing data-driven approaches. This research heralds more accurate healthcare solutions, potentially benefiting millions at risk of diabetes and facilitating proactive interventions.
ISSN:2688-0288
DOI:10.1109/SCEECS61402.2024.10481943