# AI-Based Stroke Disease Prediction System Using Real-Time Electromyography Signals
## Related Works
### Stroke Prediction Using Machine Learning and Deep Learning  
Several studies have used artificial neural networks (ANN) for stroke diagnosis or prediction ([PERSON] et al., 2016; [PERSON] et al., 2017; [PERSON] et al., 2018). Based on stroke patient data, [PERSON] et al. (2018) reported that the risk of stroke can be detected with ANN. In that study, the backpropagation algorithm was used for learning, and the consistency of prediction and diagnostic accuracy were improved. Based on ANN, [PERSON] et al. (2018) were able to predict the risk rate of stroke using 300 experimental data. As a result of the experiment, a model that can match the data of stroke patients with a high accuracy of 95.33% was proposed. However, that system only emphasizes prediction accuracy, and it is difficult to analyze the mechanics of the operating principle. [PERSON] et al. (2018) examined symptomatic intracranial hemorrhage during melting treatment for ischemic stroke, and reported a predictive method considering data from CT (Computerized Tomography) images and clinical variable values. Specifically, based on CT images of 116 ischemic stroke patients, nine out of 16 patients with symptomatic intracranial hemorrhage were successfully identified using SVMs. [PERSON] et al. (2018) reported the prediction and verification results by adjusting the parameter values of the predictive model using the kernel function of SVM for the risk factors of stroke. In particular, the RBF kernel function was used to obtain satisfactory accuracy, and this model details the process of predicting stroke risk. However, these SVM-based studies focused on predicting severity and prognosis after an outbreak, as opposed to early detection or the prediction of pre-occurrence symptoms. Further, it is hard to explain this model from an analytical point of view, as the system's operating principles are a black box; this is because it only maintains the position of increasing the accuracy of traditional stroke predictions. In addition, since it is based on images of separate clinical diagnosis and CT, it is not suitable for detecting and predicting pre-symptoms of stroke diseases using real-time bio-signals or life logs during daily life. Therefore, new research and trials are needed for the early detection of stroke onset. To overcome the shortcomings of the NIHSS studies described in Section 2.2, [PERSON] et al. (2018) published an analytical study using data mining techniques. [PERSON] et al. performed an analytical study based on the decision tree algorithm, a representative classification methodology of data mining; specifically, they attempted to automatically classify and interpret the severity of NIHSS results based on the C4.5 decision tree algorithm. In addition, by thoroughly analyzing the rules on the principle of motion, which provide additional information for the C4.5 decision tree, a very novel attempt was made in the semantic interpretation of stroke severity. However, due to the nature of the decision tree algorithm, the predictive model algorithm only provides a partial interpretation. and may require detailed analysis inherent in the data. Another study was conducted by [PERSON] et al. (2018) to predict stroke based on more than 50 risk factors for stroke. In that paper, the prediction experiment results of relatively accurate stroke are presented with algorithms such as KNN (K-nearest neighbors) and the C4.5 deception tree for data mining. However, this research methodology, like those used in previous studies, is not suitable for use as a prediction model for detecting and predicting the pre-symptoms of stroke in everyday life. Recurrent Neural Network (RNN) is a representative method of analyzing and processing time-series data in deep learning. RNNs are composed of circulatory neural networks, which are used in the current learning process along with the results of previous steps ([PERSON] et al., 2016).