COVID-19 Outbreak Prediction with Machine Learning

Sina F. Ardabili; Amir MOSAVI; Pedram Ghamisi; Filip Ferdinand; Annamaria R. Varkonyi-Koczy; Uwe Reuter; Timon Rabczuk; Peter M. Atkinson

The copyright holder for this preprint . https://doi.org/10.1101/2020.04. 17.20070094 doi: medRxiv preprint An adaptive neuro fuzzy inference system is a type of ANN based on the Takagi-Sugeno fuzzy system [86] . This approach was developed in the early 1990s. Since this system integrates the concepts of neural networks and fuzzy logic, it can take advantage of both capabilities in a unified framework. This technique is one of the most frequently used and robust hybrid ML techniques. It is consistent with a set of fuzzy if-then rules that can be learned to approximate nonlinear functions [87, 88] . Hence, ANFIS was proposed as a universal estimator. An important element of fuzzy systems is the fuzzy partition of the input space [89, 90] . For input k, the fuzzy rules in the input space make a k faces fuzzy cube. Achieving a flexible partition for nonlinear inversion is non-trivial. The idea of this model is to build a neural network whose outputs are a degree of the input that belongs to each class [91] [92] [93] . The membership functions (MFs) of this model can be nonlinear, multidimensional and, thus, different to conventional fuzzy systems [94] [95] [96] . In ANFIS, neural networks are used to increase the efficiency of fuzzy systems. The method used to design neural networks is to employ fuzzy systems or fuzzy-based structures. This model is a kind of division and conquest method. Instead of using one neural network for all the input and output data, several networks are created in this model: •