A Dimension-Reduction Based Multilayer Perception Method for Supporting the Medical Decision Making

Due to the rapid development of Medical IoT recently, how to effectively apply these huge
amounts of IoT data to enhance the reliability of the clinical decision making has become an
increasing issue in the medical field. These data usually comprise high-complicated features
with tremendous volume, and it implies that the simple inference models may less powerful to
be practiced. In deep learning, multilayer perceptron (MLP) is a kind of feed-forward artificial neural network, and it is one of the high-performance methods about stochastic scheme, fitness approximation, and regression analysis. To process these high uncertain data, the proposed work based on MLP structure in particular integrates the boosting scheme and dimension-reduction process. In this proposed work, the advanced ReLU-based activation function is used. Also, the weight initialization is applied to improve the stable prediction and convergence. After the improved dimension-reduction process is introduced, the proposed method can effectively learn the hidden information from the reformative data and the precise labels also can be recognized by stacking a small amount of neural network layers with paying few extra cost. The proposed
work shows a possible path of embedding dimension reduction in deep learning structure with minor price. In addition to the prediction issue, the proposed method can also be applied to assess risk and forecast trend among different information systems.