Early Rheumatoid Arthritis Detection by miRNA Data Analysis Using a Hybrid CNN-LSTM Deep Learning Model

Abstract

Transcriptomic data analysis has significantly evolved thanks to the high throughput data technology. Analyzing this data can notably support the early detection of several diseases. At the same time, Rheumatoid arthritis (RA) is an autoimmune disease that causes critical joints damages over time and can lead to severe disabilities. This work introduces a hybrid CNN-LSTM deep learning model that combines the CNN capabilities of higher-level feature extractions and the LSTM efficiency of determining the long-term consequences between the transcriptomic terms. This model analyzes miRNA data of Rheumatoid Arthritis patients and healthy controls to provide an early detection model for this disease. In addition, this work studies the impact of NEBNEXT and NEXTFLEX sample preparation kits on the accuracy scores of the miRNA samples' classification. The studied dataset consists of 42 miRNA files of RA cases, healthy controls, and synthetic samples. The results of the proposed model were promising, denoting sensitivity, specificity, precision, accuracy, and F1 Score values of (0.875, 0.884, 0.88, 0.883, 0.872), respectively. Moreover, comparative experiments are conducted with literature work.