Decision Support System for Predicting Survivability of Hepatitis Patients - Albogamy, Fahad R.; Asghar, Junaid; Subhan, Fazli; Asghar, Muhammad Zubair; Al-Rakhami, Mabrook S.; Khan, Aurangzeb; Nasir, Haidawati Mohamad; Rahmat, Mohd Khairil; Alam, Muhammad Mansoor; Lajis, Adidah; Su'ud, Mazliham Mohd (Frontiers in Public Health, 2022)
Background and objective: Most people die from a disease called hepatitis. Healthcare costs are costly, and hepatitis infection diagnosis and treatment demand a high degree of human skill, which creates difficulties for the medical systems in undeveloped and developing nations. Based on the findings of recent research by the World Health Organization, immunization, diagnostic tests, treatments, and information campaigns might avert an estimated 4.5 million premature deaths in low-income and middle-income regions by 2030. Because of this, it is unavoidable to create 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 the 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.