  Diabetes, resulting from inadequate insulin production or utilization, causes
extensive harm to the body. Existing diagnostic methods are often invasive and
come with drawbacks, such as cost constraints. Although there are machine
learning models like Classwise k Nearest Neighbor (CkNN) and General Regression
Neural Network (GRNN), they struggle with imbalanced data and result in
under-performance. Leveraging advancements in sensor technology and machine
learning, we propose a non-invasive diabetes diagnosis using a Back Propagation
Neural Network (BPNN) with batch normalization, incorporating data re-sampling
and normalization for class balancing. Our method addresses existing challenges
such as limited performance associated with traditional machine learning.
Experimental results on three datasets show significant improvements in overall
accuracy, sensitivity, and specificity compared to traditional methods.
Notably, we achieve accuracies of 89.81% in Pima diabetes dataset, 75.49% in
CDC BRFSS2015 dataset, and 95.28% in Mesra Diabetes dataset. This underscores
the potential of deep learning models for robust diabetes diagnosis. See
project website https://steve-zeyu-zhang.github.io/DiabetesDiagnosis/
