Semantic-Preserved Generative Adversarial Network optimized with Tyrannosaurus Optimization Algorithm for Liver Disorder Classification

Liver disease causes around two million deaths annually, accounting for 4% of global deaths. The use of algorithms
for early disease prediction in large clinical datasets shows promise, but is often constrained by the
complexity and variability of the data. Therefore, Semantic-Preserved Generative Adversarial Network optimized
using Tyrannosaurus Optimization Algorithm for Liver Disorder Classification (SPGAN-TOA-LDC) is proposed in
this paper. Input data is collected from Indian Liver Patient Dataset (ILPD). The gathered data is fed into preprocessing
stage using Distributed Set-Membership Fusion Filtering (DSMFF) to remove data redundancy. Hiking
Optimization Algorithm (HOA) is employed to select the optimal features from the dataset. The selected features
are supplied to Semantic-Preserved Generative Adversarial Network (SPGAN) to classify the liver disorder as
liver disease and non-liver disease. Generally, SPGAN does not show any adaption of maximization approaches
for identify the optimal parameters to ensure precise categorization of liver disorders. Tyrannosaurus Optimization
Algorithm (TOA) is used for improving the weight parameters of SPGAN. The proposed SPGAN-TOA-LDC
approach achieves 20.58%, 18.73% and 25.62% high accuracy, 24.58%, 28.73% and 15.62% high precision
compared with existing techniques: CNN along machine learning models under Extra Trees Classifier with MRMR
feature selection for liver disease identification on cloud (CNN-ILPD-LDC), Early identification of liver disease
utilizing hybrid soft computing methods for optimum feature selection with categorization (DNN-ILPD-EDL),
Liver disease patients prediction depending on integrated projection base statistical feature extraction and machine
learning approaches (KNN-ILPD-LDC) respectively.