A Machine Learning‐Based Framework for Accurate and Early Diagnosis of Liver Diseases: A Comprehensive Study on Feature Selection, Data Imbalance, and …

AU Rehman, WH Butt, TM Ali, S Javaid… - … Journal of Intelligent …, 2024 - Wiley Online Library
The liver is the largest organ of the human body with more than 500 vital functions. In recent
decades, a large number of liver patients have been reported with diseases such as …

[HTML][HTML] Reliable prediction of software defects using Shapley interpretable machine learning models

Y Al-Smadi, M Eshtay, A Al-Qerem, S Nashwan… - Egyptian Informatics …, 2023 - Elsevier
Predicting defect-prone software components can play a significant role in allocating
relevant testing resources to fault-prone modules and hence increasing the business value …

Exploring Innovative Approaches to Synthetic Tabular Data Generation

E Papadaki, AG Vrahatis, S Kotsiantis - Electronics, 2024 - mdpi.com
The rapid advancement of data generation techniques has spurred innovation across
multiple domains. This comprehensive review delves into the realm of data generation …

EPIC: Effective Prompting for Imbalanced-Class Data Synthesis in Tabular Data Classification via Large Language Models

J Kim, T Kim, J Choo - Advances in Neural Information …, 2025 - proceedings.neurips.cc
Large language models (LLMs) have demonstrated remarkable in-context learning
capabilities across diverse applications. In this work, we explore the effectiveness of LLMs …

An interpretable framework to identify responsive subgroups from clinical trials regarding treatment effects: Application to treatment of intracerebral hemorrhage

Y Ling, MB Tariq, K Tang, J Aronowski, Y Fann… - PLOS Digital …, 2024 - journals.plos.org
Randomized Clinical trials (RCT) suffer from a high failure rate which could be caused by
heterogeneous responses to treatment. Despite many models being developed to estimate …

Explainable machine learning approach for hepatitis C diagnosis using SFS feature selection

AM Ali, MR Hassan, F Aburub, M Alauthman… - Machines, 2023 - mdpi.com
Hepatitis C is a significant public health concern, resulting in substantial morbidity and
mortality worldwide. Early diagnosis and effective treatment are essential to prevent the …

[HTML][HTML] Generative AI: A transformative force in advancing research and care in metabolic dysfunction-associated fatty liver disease

PP Ray - Liver Research, 2024 - pmc.ncbi.nlm.nih.gov
Generative AI: A transformative force in advancing research and care in metabolic
dysfunction-associated fatty liver disease - PMC Skip to main content Here's how you know …

Utilizing Diverse Machine Learning Models for Liver Disease Patient Prediction

K Shah, A Barage, A Maluskar… - 2024 8th International …, 2024 - ieeexplore.ieee.org
The liver-damaging virus known as hepatitis C is still a major global health concern. The
need for better early detection strategies is highlighted by the serious consequences that …

The effect of Data Augmentation Using SMOTE: Diabetes Prediction by Machine Learning Techniques

A Al-Qerem, AM Ali, M Alauthman, MA Khaldy… - Proceedings of the …, 2023 - dl.acm.org
Diabetes mellitus, a severe and enduring condition characterized by impaired glucose
metabolism, poses a substantial threat to public health. Its pervasive impact continues to …

An Interpretable Causal Clustering Framework to Identify Responsive Subgroups from Clinical Trials: Application to Treatment of Intracerebral Hemorrhage

Y Ling, MB Tariq, K Tang, J Aronowski… - Available at SSRN … - papers.ssrn.com
Objective: Clinical trials suffer from a high failure rate which could be caused by
heterogeneous response to treatment. Despite many models having been developed to …