CNN-Based Analytical Approach for Liver Disease Classification and Prediction
Year: 2025
Type: conference-paper
Abstract: Liver disease is a major global health concern, and effective treatment requires an early and accurate diagnosis. Deep learning (DL) and Machine Learning (ML) techniques have been widely applied to improve disease prediction and classification. A range of models, including Convolutional Neural Networks (CNN), Support Vector Machines (SVM), Logistic Regression (LR), and K-Nearest Neighbours (KNN), were trained using the Indian Liver Patient Dataset (ILPD) from Kaggle in order to diagnose cases of liver illness. With an accuracy rate of 96.21%, precision rate of 74.76%, recall rate of 92.77%, and F1-score rate of 82.80%, the CNN model outperforms all others, demonstrating its ability to extract complex patterns for accurate categorization. Experimental results prove that Deep Learning (DL) methodologies, especially CNN, can enhance predictive power extensively compared to Machine Learning (ML) methods.
Authors: Abrar Ahmed Syed, Asia Banu Shaik, Sukanya Konatam, Maddi Venkata Phani Kumar, Neha Gupta
Institution: The University of Texas at Austin
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4410810051