Motico: An attentional mechanism network model for smart aging disease risk prediction based on image data classification

Abstract

The current disease risk prediction model with many parameters is complex to run smoothly on mobile terminals such as tablets and mobile phones in imaginative elderly care application scenarios. In order to further reduce the number of parameters in the model and enable the disease risk prediction model to run smoothly on mobile terminals, we designed a model called Motico (An Attention Mechanism Network Model for Image Data Classification). During the implementation of the Motico model, in order to protect image features, we designed an image data preprocessing method and an attention mechanism network model for image data classification. The Motico model parameter size is only 5.26 MB, and the memory only takes up 135.69 MB. In the experiment, the accuracy of disease risk prediction was 96 %, the precision rate was 97 %, the recall rate was 93 %, the specificity was 98 %, the F1 score was 95 %, and the AUC was 95 %. This experimental result shows that our Motico model can implement classification prediction based on the image data classification attention mechanism network on mobile terminals.