The combination of various sensors with different data methods is a common technique used to increase precision in the classification of IoT health data. However, for even the assessment outcomes, all modalities are barely available and this scarcity of evidence poses significant barriers to multimodal education. Driven by recent developments in deep education, we are providing a cross-neural network for the segmentation of the IoT Health Data Classification, which is trained on data modalities not all available during trials. In IoT Health Data Classification, we train our architecture with a cost function that is especially tailored to unbalanced classes. We are providing the device with a benchmark data set with incomplete data. Assuming that they are not present in the research process, our methodology goes beyond both the CNN training and the collection of two CNNs trained in the missing modality by utilising time data