CH.Sai Sushmitha, B.Bhagya Lakshmi 03 06 Liver diseases represent a major global health challenge due to their asymptomatic nature in early stages and high mortality rates when detected late Accurate and early diagnosis is crucial for effective treatment and improved patient outcomes This paper proposes a hybrid intelligent system that integrates Genetic Algorithm GA for feature selection and optimization with Convolutional Neural Networks CNN for automated classification of liver diseases The Genetic Algorithm enhances the model performance by selecting the most relevant features from medical datasets thereby reducing dimensionality and improving computational efficiency The CNN model is employed to learn complex patterns from imaging or structured clinical data The proposed system demonstrates improved accuracy sensitivity and specificity compared to traditional machine learning approaches Experimental results validate the robustness and reliability of the hybrid model in diagnosing liver diseases effectively Copyright (c) Journal of Science Engineering Technology and Management Science. All rights reserved 2026