Decision Support System for Predicting Survivability of Hepatitis Patients

Frontiers in Public Health

article

Fahad R. Albogamy¹  Junaid Asghar²  Fazli Subhan³  Muhammad Zubair Asghar⁵  Mabrook S. Al-Rakhami⁷  Aurangzeb Khan³  Haidawati Mohamad Nasir⁵  Mohd Khairil Rahmat⁵  Muhammad Mansoor Alam⁵  Adidah Lajis⁵  Mazliham Mohd Su'ud³ 

[1] Computer Sciences Program, Turabah University College, Taif University;Faculty of Pharmacy, Gomal University;Faculty of Engineering and Computer Sciences, National University of Modern Languages-NUML;Faculty of Computer and Information, Multimedia University;Center for Research and Innovation, CoRI, Universiti Kuala Lumpur;Institute of Computing and Information Technology, Gomal University;Division of Pervasive and Mobile Computing, Information Systems Department, College of Computer and Information Sciences, King Saud University;Department of Computer Science, University of Science and Technology;Faculty of Computing, Riphah International University;Malaysian Institute of Information Technology, University of Kuala Lumpur;Faculty of Computing and Informatics, Multimedia University;Faculty of Engineering and Information Technology, School of Computer Science, University of Technology Sydney

关键词: disease diagnosis;    deep learning;    hepatitis diagnostics;    decision support system;    bidirectional LSTM;   

DOI  :  10.3389/fpubh.2022.862497

学科分类：社会科学、人文和艺术（综合）

来源: Frontiers

【 摘 要 】

Background and Objective Viral hepatitis is a major public health concern on a global scale. It predominantly affects the world's least developed countries. The most endemic regions are resource constrained, with a low human development index. Chronic hepatitis can lead to cirrhosis, liver failure, cancer and eventually death. Early diagnosis and treatment of hepatitis infection can help to reduce disease burden and transmission to those at risk of infection or reinfection. Screening is critical for meeting the WHO's 2030 targets. Consequently, automated systems for the reliable prediction of hepatitis illness. When applied to the prediction of hepatitis using imbalanced datasets from testing, machine learning (ML) classifiers and known methodologies for encoding categorical data have demonstrated a wide range of unexpected results. Early research also made use of an artificial neural network to identify features without first gaining a thorough understanding of the sequence data. Methods To help in accurate binary classification of diagnosis (survivability or mortality) in patients with severe hepatitis, this paper suggests a deep learning-based decision support system (DSS) that makes use of bidirectional long/short-term memory (BiLSTM). Balanced data was utilized to predict hepatitis using the BiLSTM model. Results In contrast to previous investigations, the trial results of this suggested model were encouraging: 95.08% accuracy, 94% precision, 93% recall, and a 93% F1-score. Conclusions In the field of hepatitis detection, the use of a BiLSTM model for classification is better than current methods by a significant margin in terms of improved accuracy.

【 授权许可】

CC BY