Predicting the Status of Thyroid and Cardiovascular Patients According to Their Electronic Records Using Temporal Elements Based on the Combination of Shuffled Frog Leaping Algorithm (SFLA) and Deep Learning

The field of health and treatment is known as one of the most important application fields of information technology, in which, the problem of predicting a disease appears to be highly important. In fact, such predictions are made by the physician 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. all pursue the common goal of facilitating the treatment process. For, the patient can receive the best services within the shortest time due to the availability and having these systems and information, and the doctor can provide services to his patient from anywhere in the world at any time. 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 and feature selection and classification, respectively. Two datasets of cardiovascular diseases 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 other similar methods in the evaluated datasets. In the cardiovascular diseases dataset, this improvement was recorded as ۱.۴% and ۳.۲% compared to the author's previous method and the updated similar method, respectively.

کلیدواژه ها:

Prediction of patients' conditions ، electronic file ، shuffled frog leaping algorithm (SFLA) ، deep learning