Diabetes prediction using a context-adaptive activation neural network

Abstract

This paper describes a novel diabetes prediction framework underpinned by a neural network architecture which features a parameterized activation function, called the Self-Learning Activation Linear Unit (SLALU). The proposed diabetes prediction model is composed of multiple self-learning activation linear units with parameters, and this SLALU can fit most of the traditional activation functions, including but not limited to the Rectified Linear Unit (ReLU) and the Parametric Rectified Linear Unit (PReLU). This self-adjusting activation approach provides a flexible bending ability, thereby outperforming the bending capabilities of traditional activation functions and reducing the intricacy involved in their manual selection. The design of our neuron model improves the network’s representational capacity and fitting ability. Deployed in diabetes prediction, our advanced network model demonstrated superior performance across a suite of metrics, thereby recording an accuracy of 98.18%, thus outperforming several existing models.

Keywords

diabetes prediction / attention mechanism / deep neural networks / adjustable activation neural networks

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