[HTML][HTML] Impact of the learners diversity and combination method on the generation of heterogeneous classifier ensembles
Ensembles of classifiers is a proven approach in machine learning with a wide variety of
research works. The main issue in ensembles of classifiers is not only the selection of the …
research works. The main issue in ensembles of classifiers is not only the selection of the …
Automatic detection of Alzheimer's disease progression: An efficient information fusion approach with heterogeneous ensemble classifiers
Predicting Alzheimer's disease (AD) progression is crucial for improving the management of
this chronic disease. Usually, data from AD patients are multimodal and time series in …
this chronic disease. Usually, data from AD patients are multimodal and time series in …
Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease
Background and objectives Parkinson's Disease (PD) is a devastating chronic neurological
condition. Machine learning (ML) techniques have been used in the early prediction of PD …
condition. Machine learning (ML) techniques have been used in the early prediction of PD …
Intensive care unit mortality prediction: An improved patient-specific stacking ensemble model
The intensive care unit (ICU) admits the most seriously ill patients requiring extensive
monitoring. Early ICU mortality prediction is crucial for identifying patients who are at great …
monitoring. Early ICU mortality prediction is crucial for identifying patients who are at great …
Restricted Boltzmann machine assisted secure serverless edge system for internet of medical things
The Internet of things (IoT) is a network of technologies that support a wide variety of
healthcare workflow applications to facilitate users' obtaining real-time healthcare services …
healthcare workflow applications to facilitate users' obtaining real-time healthcare services …
Computer aided progression detection model based on optimized deep LSTM ensemble model and the fusion of multivariate time series data
Alzheimer's disease (AD) is the most common form of dementia. Early and accurate
detection of AD is crucial to plan for disease modifying therapies that could prevent or delay …
detection of AD is crucial to plan for disease modifying therapies that could prevent or delay …
Diagnose Diabetic Mellitus Illness Based on IoT Smart Architecture
Obtaining a quick remote diagnosis of heart disease has proven problematic in recent days.
To overcome such issues in e‐Healthcare systems, Internet of Things (IoT) applications …
To overcome such issues in e‐Healthcare systems, Internet of Things (IoT) applications …
Advanced techniques for predicting the future progression of type 2 diabetes
MS Islam, MK Qaraqe, SB Belhaouari… - IEEE …, 2020 - ieeexplore.ieee.org
Diabetes is a costly and burdensome metabolic disorder that occurs due to the elevation of
glucose levels in the bloodstream. If it goes unchecked for an extended period, it can lead to …
glucose levels in the bloodstream. If it goes unchecked for an extended period, it can lead to …
Accelerating retinal fundus image classification using artificial neural networks (ANNs) and reconfigurable hardware (FPGA)
Diabetic retinopathy (DR) and glaucoma are common eye diseases that affect a blood
vessel in the retina and are two of the leading causes of vision loss around the world …
vessel in the retina and are two of the leading causes of vision loss around the world …
[HTML][HTML] Stacking ensemble approach to diagnosing the disease of diabetes
A Daza, CFP Sánchez, G Apaza-Perez, J Pinto… - Informatics in Medicine …, 2024 - Elsevier
Background Diabetes is a very common disease today and has acquired a worrying focus in
the field of public health globally, in fact, it is estimated that the number of people with …
the field of public health globally, in fact, it is estimated that the number of people with …