Open AccessComprehensive Performance Assessment of Deep Learning Models in Early Prediction and Risk Identification of Chronic Kidney Disease
Author(s) -
Shamima Akter,
Ahsan Habib,
Md. Ashiqul Islam,
Md. Sagar Hossen,
Wasik Ahmmed Fahim,
Puza Rani Sarkar,
Manik Ahmed
Publication year - 2021
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2021.3129491
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
The incidence of chronic kidney disease (CKD) is rising rapidly around the globe. Asymptomatic CKD is common and guideline-directed monitoring to predict CKD by various factors is underutilized. Computer-aided automated diagnostic (CAD) can play a major role to predict CKD. CAD systems such as deep learning algorithms are pivotal in disease diagnosis due to their high classification accuracy. In this paper, various clinical features of CKD were utilized and seven state-of-the-art deep learning algorithms (ANN, LSTM, GRU, Bidirectional LSTM, Bidirectional GRU, MLP, and Simple RNN) were implemented for the prediction and classification of CKD. The proposed algorithms were applied based on artificial intelligence by extracting and evaluating features using five different approaches from pre-processed and fitted CKD datasets. In this study, we have measured accuracy, precision, recall, and calculated the loss and validation loss in prediction. Further, the study analyzed computation time and prediction ratio, and AUC to evaluate the model performance along with statistical significance to compare their performances. While classifying CKD, algorithms such as ANN, Simple RNN, and MLP provided high accuracy of 99%, 96%, 97% respectively, and a good prediction ratio along with reduced time. The model outperforms traditional data classification techniques by providing superior predictive ability. Subsequently, the study proposed the integration of best performing DL models in the IoMT. This proposal will assist predictive analytics to advance CKD prediction by using deep learning more efficiently and effectively. The study is the first fundamental step toward a comprehensive performance assessment to classify and predict CKD using deep learning models and its associated risk factors.