Diabetes Prediction and Detection System Through a Recurrent Neural Network in a Sensor Device

Diabetes is a significant global health issue that demands accurate, accessible, and non-invasive diagnostic methods for effective prevention and treatment. Conventional diagnostic systems are often expensive, painful, time-consuming, and not universally available. In this study, we present a smart system for acetone estimation using simulated breath and a recurrent neural network (RNN) model. The detection system employs a new sensor fabricated from a composite of 1D nanostructured KWO (K2W7O22) and 2D nanosheet MXene (Ti3C2), designed to measure the chemiresistive response to acetone by mimicking human breath. Resistance data collected by the sensor are used to compute sensitivity values for each acetone concentration (in parts per million, PPM). These values serve as input features for the RNN model, which learns to evaluate health as healthy, high-risk, or diabetic. Trained on acetone concentrations ranging from 0.4 to 2 PPM, the RNN achieves an R2 of 99.41% in predicting potential for accurate breath acetone prediction. In future work, we aim to develop a smart device and mobile application based on this model to facilitate real-time diabetes monitoring and prediction.