Unified time series analysis with Bi-long short-term memory model for early prediction of dyslipidemia in steel workers

Abstract

Dyslipidemia is a serious problem in steel workers, which needs to be detected in the early stage. However, the state of art machine learning methods failed to provide accurate classification, which resulted in reduced accuracy. So, this article aimed topredict dyslipidemia among steel workers using a recurrent neural network (RNN) with a bidirectional long short-term memory (Bi-LSTM) architecture. Initially, the data on the steel workers and their lipid profiles are collected and preprocessed by normalizing and scaling to ensure that all the features have the same scale and variance. An RNN model with a Bi-LSTM architecture is built and trained on the training set, using Adam loss functions and optimizer. The proposed RNN-BiLSTM model had an accuracy of 93.13%, specificity of 91.23%, precision of 92.23%, recall of 93.45%, and F1-score of 94.03%, which are superior to existing methods.