Diabetes Prediction Using the Ghost Deep Learning Model

Abstract

The article presents an innovative approach for diabetes prediction by applying a phantom deep learning model, designed to optimize accuracy and efficiency in early diagnosis. This model uses a robust clinical data set, integrating advanced artificial intelligence techniques with a lightweight and efficient architecture, allowing computational costs to be significantly reduced without sacrificing predictive performance. Key features of the model include its ability to handle imbalanced data sets, common in medical environments, and its adaptability to various clinical contexts, making it especially versatile. Experimental results demonstrate that the phantom deep learning model greatly outperforms conventional methods such as artificial neural networks (ANNs) in terms of accuracy and efficiency. Specifically, the phantom model achieved an accuracy of 79.24% without overfitting in identifying patients at risk of diabetes, compared to the 88.57% obtained by the conventional ANN model with overfitting. In addition, the loss of the phantom model has a greater generalization capacity and lower error in predictions. These results highlight the superiority of the phantom model in terms of accuracy and stability. Its scalability and low resource requirements make it a viable option for implementation in public and private health systems; The phantom deep learning model represents a significant advance in the application of artificial intelligence in the healthcare field.