Tabular Data Generation to Improve Classification of Liver Disease Diagnosis

• Published in: Applied sciences 2023-02, Vol.13 (4), p.2678
• Main Authors: Alauthman, Mohammad, Aldweesh, Amjad, Al-qerem, Ahmad, Aburub, Faisal, Al-Smadi, Yazan, Abaker, Awad M, Alzubi, Omar Radhi, Alzubi, Bilal
• Format: Article
• Language: English
• Subjects: Alcoholism; Algorithms; Classification; classifications; data augmentation; Data mining; Diagnosis; Drug abuse; GAN; Health aspects; Hepatic encephalopathy; Hepatitis; India; Jaundice; Laboratories; Learning algorithms; Liver; Liver cancer; Liver cirrhosis; Liver diseases; Machine learning; Medical diagnosis; Medical records; Medical research; Medicine, Experimental; Mortality; Neural networks; Performance assessment; Performance evaluation; Random variables; Renal failure; Skin; Statistical analysis; Support vector machines; Vomiting
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13042678

Liver diseases are among the most common diseases worldwide. Because of the high incidence and high mortality rate, these diseases diagnoses are vital. Several elements harm the liver. For instance, obesity, undiagnosed hepatitis infection, and alcohol abuse. This causes abnormal nerve function, bloody coughing or vomiting, insufficient kidney function, hepatic failure, jaundice, and liver encephalopathy.. The diagnosis of this disease is very expensive and complex. Therefore, this work aims to assess the performance of various machine learning algorithms at decreasing the cost of predictive diagnoses of chronic liver disease. In this study, five machine learning algorithms were employed: Logistic Regression, K-Nearest Neighbor, Decision Tree, Support Vector Machine, and Artificial Neural Network (ANN) algorithm. In this work, we examined the effects of the increased prediction accuracy of Generative Adversarial Networks (GANs) and the synthetic minority oversampling technique (SMOTE). Generative opponents’ networks (GANs) are a mechanism to produce artificial data with a distribution close to real data distribution. This is achieved by training two different networks: the generator, which seeks to produce new and real samples, and the discriminator, which classifies the augmented samples using supervised classifications. Statistics show that the use of increased data slightly improves the performance of the classifier.