Machine-learning-based disease diagnosis: A comprehensive review
Globally, there is a substantial unmet need to diagnose various diseases effectively. The
complexity of the different disease mechanisms and underlying symptoms of the patient …
complexity of the different disease mechanisms and underlying symptoms of the patient …
Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer's disease dementia: a systematic review
S Grueso, R Viejo-Sobera - Alzheimer's research & therapy, 2021 - Springer
Background An increase in lifespan in our society is a double-edged sword that entails a
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
growing number of patients with neurocognitive disorders, Alzheimer's disease being the …
A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease
Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and
progression detection have been intensively studied. Nevertheless, research studies often …
progression detection have been intensively studied. Nevertheless, research studies often …
Machine learning techniques for the diagnosis of Alzheimer's disease: A review
Alzheimer's disease is an incurable neurodegenerative disease primarily affecting the
elderly population. Efficient automated techniques are needed for early diagnosis of …
elderly population. Efficient automated techniques are needed for early diagnosis of …
Random forest algorithm for the classification of neuroimaging data in Alzheimer's disease: a systematic review
Objective: Machine learning classification has been the most important computational
development in the last years to satisfy the primary need of clinicians for automatic early …
development in the last years to satisfy the primary need of clinicians for automatic early …
Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data
Background Above-ground biomass (AGB) is a basic agronomic parameter for field
investigation and is frequently used to indicate crop growth status, the effects of agricultural …
investigation and is frequently used to indicate crop growth status, the effects of agricultural …
Random forest
SJ Rigatti - Journal of Insurance Medicine, 2017 - meridian.allenpress.com
For the task of analyzing survival data to derive risk factors associated with mortality,
physicians, researchers, and biostatisticians have typically relied on certain types of …
physicians, researchers, and biostatisticians have typically relied on certain types of …
Predicting factors for survival of breast cancer patients using machine learning techniques
Background Breast cancer is one of the most common diseases in women worldwide. Many
studies have been conducted to predict the survival indicators, however most of these …
studies have been conducted to predict the survival indicators, however most of these …
Artificial intelligence in neurodegenerative diseases: A review of available tools with a focus on machine learning techniques
Neurodegenerative diseases have shown an increasing incidence in the older population in
recent years. A significant amount of research has been conducted to characterize these …
recent years. A significant amount of research has been conducted to characterize these …
Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images
Abstract The Local Climate Zone (LCZ) scheme is a classification system providing a
standardization framework to present the characteristics of urban forms and functions …
standardization framework to present the characteristics of urban forms and functions …