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A Machine Learning‐Based Framework for Accurate and Early Diagnosis of Liver Diseases: A Comprehensive Study on Feature Selection, Data Imbalance, and …
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 …
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
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 …
relevant testing resources to fault-prone modules and hence increasing the business value …
Exploring Innovative Approaches to Synthetic Tabular Data Generation
The rapid advancement of data generation techniques has spurred innovation across
multiple domains. This comprehensive review delves into the realm of data generation …
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
Large language models (LLMs) have demonstrated remarkable in-context learning
capabilities across diverse applications. In this work, we explore the effectiveness of LLMs …
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
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 …
heterogeneous responses to treatment. Despite many models being developed to estimate …
Explainable machine learning approach for hepatitis C diagnosis using SFS feature selection
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 …
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 …
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 …
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
Diabetes mellitus, a severe and enduring condition characterized by impaired glucose
metabolism, poses a substantial threat to public health. Its pervasive impact continues to …
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
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 …
heterogeneous response to treatment. Despite many models having been developed to …