Jul 2026· Network Modeling Analysis in Health Informatics and Bioinformatics· Vol 15· 0 citations· 33 references
TL;DR
A comprehensive comparative framework integrating classical ML classifiers such as Support Vector Machine, K-Nearest Neighbors, Decision Tree, Logistic Regression, and Random Forest with DL models including Multilayer Perceptron, Long Short-Term Memory, Recurrent Neural Network, Convolutional Neural Network, and Deep Neural Network for liver disease prediction is proposed.
Experimental results demonstrate that the proposed system can effectively assist in the early screening of liver disease, offering a low-cost, accessible, and user-friendly diagnostic aid that bridges the gap between patients and healthcare providers.
M.Dhana Laxmi, S. Nikitha· International Journal of Sci...· 0 citations
An effective machine learning and deep learning framework to early-stage predict the Chronic Kidney Disease (CKD) and chronic heart disease using structured clinical data available in the UCI Machine Learning Repository is presented.
P. Pragathi, Neha Gupta· Journal of Advances in Scien...· 0 citations
This intelligent system effectively predicts various thyroid conditions, including primary hypothyroidism and concurrent non-thyroidal illness offering counselors and clinicians a reliable auxiliary tool to identify physiological root causes of patient distress.
Abraham Osemeke Agbonifo· International Journal of Med...· 0 citations
The reviewed studies demonstrated that Deep learning approaches, particularly convolutional neural networks, transformer-based models, and multimodal frameworks, showed improved predictive accuracy when large and diverse datasets were available.
Tehreem Khan, Tayyaba Usman, Ifrah Khalid et al.· Discover Artificial Intellig...· 0 citations
The main objective of this study is to develop an interpretable soft-computing model that can classify patients as having liver disease or not and the results demonstrate that the proposed approach is highly accurate and suitable for real-world liver disease detection.
Pranabes Gangopadhyay, Tirtharaj Sen, Bidesh Chakraborty et al.· Discover Applied Sciences· 0 citations
Evidence is provided that ensemble-based frameworks currently offer the most effective balance between predictive accuracy, robustness, and clinical feasibility, and future research should emphasize multi-center external validation and explainable AI frameworks.
Marium Shaikh, Hanmant Fadewar· International Journal For Mu...· 0 citations
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