Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020)
Understanding the data and reaching accurate conclusions are of paramount importance in
the present era of big data. Machine learning and probability theory methods have been …
the present era of big data. Machine learning and probability theory methods have been …
[HTML][HTML] Supervised machine learning in drug discovery and development: Algorithms, applications, challenges, and prospects
Drug discovery and development is a time-consuming process that involves identifying,
designing, and testing new drugs to address critical medical needs. In recent years, machine …
designing, and testing new drugs to address critical medical needs. In recent years, machine …
Efficient deep neural networks for classification of Alzheimer's disease and mild cognitive impairment from scalp EEG recordings
The early diagnosis of subjects with mild cognitive impairment (MCI) is an effective
appliance of prognosis of Alzheimer's disease (AD). Electroencephalogram (EEG) has many …
appliance of prognosis of Alzheimer's disease (AD). Electroencephalogram (EEG) has many …
Alzheimer's disease and frontotemporal dementia: A robust classification method of EEG signals and a comparison of validation methods
Dementia is the clinical syndrome characterized by progressive loss of cognitive and
emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer's …
emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer's …
[Retracted] Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer's Disease Detection
M Kamal, AR Pratap, M Naved… - Computational …, 2022 - Wiley Online Library
Alzheimer's disease is characterized by the presence of abnormal protein bundles in the
brain tissue, but experts are not yet sure what is causing the condition. To find a cure or …
brain tissue, but experts are not yet sure what is causing the condition. To find a cure or …
High‐order resting‐state functional connectivity network for MCI classification
Brain functional connectivity (FC) network, estimated with resting‐state functional magnetic
resonance imaging (RS‐fMRI) technique, has emerged as a promising approach for …
resonance imaging (RS‐fMRI) technique, has emerged as a promising approach for …
[HTML][HTML] Bayesian networks for risk prediction using real-world data: a tool for precision medicine
Objective The fields of medicine and public health are undergoing a data revolution. An
increasing availability of data has brought about a growing interest in machine-learning …
increasing availability of data has brought about a growing interest in machine-learning …
[HTML][HTML] Applying naive bayesian networks to disease prediction: a systematic review
M Langarizadeh, F Moghbeli - Acta Informatica Medica, 2016 - ncbi.nlm.nih.gov
Objective: This paper aims to review published evidence about the application of NBNs in
predicting disease and it tries to show NBNs as the fundamental algorithm for the best …
predicting disease and it tries to show NBNs as the fundamental algorithm for the best …
Advancing legal recommendation system with enhanced Bayesian network machine learning
The integration of machine learning algorithms into the legal recommendation system marks
a burgeoning area of research, with a particular focus on enhancing the accuracy and …
a burgeoning area of research, with a particular focus on enhancing the accuracy and …
Uncertainties in conditional probability tables of discrete Bayesian Belief Networks: A comprehensive review
J Rohmer - Engineering Applications of Artificial Intelligence, 2020 - Elsevier
Abstract Discrete Bayesian Belief Network (BBN) has become a popular method for the
analysis of complex systems in various domains of application. One of its pillar is the …
analysis of complex systems in various domains of application. One of its pillar is the …