# Recurrent Neural Network-Based Multimodal Deep Learning for Estimating Missing Values in Healthcare
## 2 Related Work  
### Research Trends of Soft Computing  
Soft computing is a technique for dealing with inaccurate and uncertain data that are not easily or cannot at all be modeled mathematically. It includes fuzzy logic, evolutionary computing, swarm intelligence, and deep learning [11; 12]. Fuzzy logic is used to support decision making in a real-world situation where it is difficult to divide an issue accurately and dichotomously. Propositions that have different and ambiguous criteria according to the individual, such as large, small, cold, and hot, are changed to those that can be understood by a computer. Unclear or subjective criteria are explained by means of sets, hedges, rules, and inferences. Evolutionary computing is used to conduct repeated searches to design a better model for machine learning, just as nature attempts to find better genes. It finds the highest goodness-of-fit model through the repeated encoding and evaluation of a given issue. Swarm intelligence is used to allow objects with similar characteristics to solve a given problem according to rules. This means that multiple objects taking action according to simple rules interact with each other and the overall action becomes intelligent. Each object repeats its simple rules without central control, and the objects gather together to appear as one artificial intelligence system. Swarm intelligence is found mainly in logistic robots, smart factories, and the IoT.  
Deep learning, one of the machine learning areas, is based on artificial neural networks (ANNs) combined with massive data. To find an answer to a given problem, a weight is updated repeatedly by using refined training data. Currently, deep learning shows high accuracy rates in cognitive areas, including those of images, video, and voice. A variety of services using deep learning has been developed. Thus, soft computing has led to significant results in the fields of global optimization and nonlinear data processing and has attracted considerable attention in the imaging processing field in particular. Soft computing applied to image processing provides a useful tool for decision makers in multiple application areas, including agriculture, mineralogy, transportation, and security. For example, a change in radar images is detected by using an RBM, or a monthly rainfall map is established by using a fuzzy model. Thus, soft computing is applied in many different industries. In addition, fuzzy logic can be used for various applications, such as the extraction of information from remote detection images, change detection, splitting, bandwidth selection, classification, separation, and clustering [11; 12; 13; 14]. [PERSON] et al. [13] proposed adaptive neuro-fuzzy inference systems (ANFISs) for predicting software defects. Software defect prediction by means of soft computing is used to predict the possibility of a defect occurring. ANFISs were developed to combine the advantages of fuzzy inference systems and ANNs. They consist of four layers: fuzzing, rule execution, normalization, and nonfuzzing. They predict a software defect in the sequence of the layers. [PERSON] et al. [14] used soft computing-based hybrid modeling to predict urban traffic flow. They proposed a modeling approach that implements hybrid modeling, using a combination of ANNs and a statistical approach, for predicting traffic flow in the unit of one hour. Based on the ensemble technique, their approach generated a prediction from multiple models for the same variable at the same time. By generating a prediction through multiple routes for a single object, it is possible to collect diverse candidate values. Using the results from multiple models, their method estimated an optimal value. Thus, it is possible to create the model with the highest accuracy for one variable.