Genetic learning models in predicting the risk of neurological diseases: the relationship between genetics and neurological risk factors using artificial intelligence algorithms.

Artificial intelligence generally refers to the ability of systems and programs to perform tasks that appear to require human understanding and decision making. In the field of health and treatment, artificial intelligence is known as a powerful and innovative tool that can bring significant improvements in the diagnosis, prevention and treatment of diseases. With the increasing progress in the field of genetics and artificial intelligence, the combination of these two sciences in order to predict the risk of neurological diseases can be an important step in the field of medicine. Genetic learning models, by examining genetic connections and neurological risk factors, increase the ability to predict the risk of neurological diseases. This article examines the relationship between concepts such as deep neural networks, recurrent networks, reinforcement learning machines, dimensionality reduction algorithms and data mining. Next, it deals with the applications of artificial intelligence algorithms in the analysis of genetic data. Then, it introduces the genetic learning models that are used in the investigation of genetic communication and neurological risk factors. This research presents the relationship between different fields of artificial intelligence and genetics to improve the detailed analysis of genetic data and the diagnosis of neurological risk factors. It is hoped that this article will lead to further advances in the diagnosis and prevention of neurological diseases and help improve the health of society.

Keywords:

genetic learning , artificial intelligence algorithms , neurological risk factors , neurological diseases