Efficient Processing and Intelligent Diagnosis Algorithm for Internet of Things Medical Data Based on Deep Learning

Electronic Medical Record (EMR) is a commonly used tool in medical diagnosis, which has static recording, difficulty in combining and analyzing different forms of data, and insufficient diagnostic efficiency and accuracy. This article proposes a CNN (Convolutional Neural Network)-LSTM (Long Short-Term Memory) algorithm for efficient processing and intelligent diagnosis of Internet of Things (IoT) medical data. The Word2Vec model is applied to clinical text data and its ability is utilized to capture semantic relationships between words. Medical image data is feature extracted using CNN, while physiological signal data is dynamically processed using LSTM to identify trends and anomalies in the data. An attention mechanism is applied to dynamically adjust the model’s attention weights for different types of data. By analyzing the samples of health, cardiovascular disease, diabetes, chronic obstructive pulmonary disease, hypertension, and chronic kidney disease, the CNN-LSTM in this article can accurately classify a variety of diseases, and the accuracy rate of healthy individuals has reached 97.8%. By combining CNN-LSTM with multimodal data, the accuracy and efficiency of medical diagnosis have been effectively improved.

@article{Liyun2025,
  title     = {Efficient Processing and Intelligent Diagnosis Algorithm for Internet of Things Medical Data Based on Deep Learning},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {5},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Wang Liyun},
  doi       = {10.14569/IJACSA.2025.0160585},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160585}
}