Abstract
Health and treatment are two of the most important application fields of information technology, in which the problem of predicting a disease is highly important. The physician makes such predictions based on the clinical condition of the patient and the level of facilities and advances in medical knowledge for the patient—information technology benefits from multiple methods to help this field. Accordingly, the patient information storage system, drug information, treatment and surgery systems, treatment follow-up systems, remote treatment systems, etc., aim to facilitate the treatment process. The patient can receive the best services within the shortest time due to these systems and information availability. The doctor can provide services to his patient anywhere in the world. This paper provided a model to predict the condition of patients based on their electronic records using temporal elements based on combining the shuffled frog leaping algorithm (SFLA) and deep learning. Accordingly, the evolutionary shuffled frog leaping algorithm (SFLA) and deep learning were used for preprocessing, feature selection, and classification. Two datasets of cardiovascular and thyroid diseases were utilized in the simulation section to ensure the efficiency of the proposed method. Based on this simulation, the proposed method indicated improvement compared to similar methods in the evaluated datasets. In the cardiovascular diseases dataset, this improvement was recorded as 1.4% and 3.2% compared to the author's previous and updated similar methods, respectively.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.