An ensemble-based machine learning solution for imbalanced multiclass dataset during lithology log generation
The lithology log, an integral component of the master log, graphically portrays the
encountered lithological sequence during drilling operations. In addition to offering real-time …
encountered lithological sequence during drilling operations. In addition to offering real-time …
Multiple Explainable Approaches to Predict the Risk of Stroke Using Artificial Intelligence
Stroke occurs when a brain's blood artery ruptures or the brain's blood supply is interrupted.
Due to rupture or obstruction, the brain's tissues cannot receive enough blood and oxygen …
Due to rupture or obstruction, the brain's tissues cannot receive enough blood and oxygen …
Test input prioritization for machine learning classifiers
Machine learning has achieved remarkable success across diverse domains. Nevertheless,
concerns about interpretability in black-box models, especially within Deep Neural Networks …
concerns about interpretability in black-box models, especially within Deep Neural Networks …
An interpretable approach using hybrid graph networks and explainable AI for intelligent diagnosis recommendations in chronic disease care
M Huang, XS Zhang, UA Bhatti, YY Wu, Y Zhang… - … Signal Processing and …, 2024 - Elsevier
With the rapid advancement of modern medical technology and the increasing demand for a
higher quality of life there is an emergent requirement for personalized healthcare services …
higher quality of life there is an emergent requirement for personalized healthcare services …
A Comparative Analysis of LIME and SHAP Interpreters with Explainable ML-Based Diabetes Predictions
Explainable artificial intelligence is beneficial in converting opaque machine learning
models into transparent ones and outlining how each one makes decisions in the healthcare …
models into transparent ones and outlining how each one makes decisions in the healthcare …
Empowering Glioma Prognosis With Transparent Machine Learning and Interpretative Insights Using Explainable AI
A Palkar, CC Dias, K Chadaga, N Sampathila - IEEE Access, 2024 - ieeexplore.ieee.org
The primary objective of this research is to create a reliable technique to determine whether
a patient has glioma, a specific kind of brain tumour, by examining various diagnostic …
a patient has glioma, a specific kind of brain tumour, by examining various diagnostic …
[HTML][HTML] On the diagnosis of chronic kidney disease using a machine learning-based interface with explainable artificial intelligence
Abstract Chronic Kidney Disease (CKD) is increasingly recognised as a major health
concern due to its rising prevalence. The average survival period without functioning …
concern due to its rising prevalence. The average survival period without functioning …
Synergistic Feature Engineering and Ensemble Learning for Early Chronic Disease Prediction
HA Al-Jamimi - IEEE Access, 2024 - ieeexplore.ieee.org
Chronic diseases, a global public health challenge, necessitate the deployment of cutting-
edge predictive models for early diagnosis and personalized interventions. This study …
edge predictive models for early diagnosis and personalized interventions. This study …
An effective role-oriented binary Walrus Grey Wolf approach for feature selection in early-stage chronic kidney disease detection
In clinical decision-making for chronic disorders like chronic kidney disease, high variability
often leads to uncertainty and negative outcomes. Deep learning techniques have been …
often leads to uncertainty and negative outcomes. Deep learning techniques have been …
Deep Learning for Detecting and Early Predicting Chronic Obstructive Pulmonary Disease from Spirogram Time Series: A UK Biobank Study
S Mei, Y Zhou, J Xu, Y Wan, S Cao, Q Zhao… - arXiv preprint arXiv …, 2024 - arxiv.org
Chronic Obstructive Pulmonary Disease (COPD) is a chronic inflammatory lung condition
that causes airflow obstruction. The existing methods can only detect patients who already …
that causes airflow obstruction. The existing methods can only detect patients who already …