GL-LSTM Model For Multi-label Text Classification Of Cardiovascular Disease Reports

In recent years, the rapid growth of electronic data and information has gotten a lot of attention to find relevant knowledge such as textual information. The goal of automatic text classification is to automatically predict textual articles classes, especially in the medical domain. However, for some applications, the used data must inherently be described by more than one label. In this research, a new scheme of medical multi-label text classification is investigated which is based on intelligent engineering features using GloVe technique and LSTM classifier. The main particularity of GloVe permits the extraction of informative features to the word level automatically and capture the global and local textual semantics. The choice of the LSTM model is motivated by the success that has been achieved by taking into account the very long-term dependencies between words. The experiment of our approach named GL-LSTM based on Ohsumed cardiovascular text dataset has produced impressive results with an overall accuracy of 0.927 compared with related works existing in the literature