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 …

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 …

Machine learning (ML) has been instrumental in solving complex problems and significantly
advancing different areas of our lives. Decision tree-based methods have gained significant …

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 …

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 …

Bayesian networks (BNs) are probabilistic graphical models that leverage Bayes' theorem to
portray dependencies and cause-and-effect relationships between variables. These …

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 …

Brain functional connectivity (FC) network, estimated with resting‐state functional magnetic
resonance imaging (RS‐fMRI) technique, has emerged as a promising approach for …

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 …

Introduction: Naive Bayesian networks (NBNs) are one of the most effective and simplest
Bayesian networks for prediction. Objective: This paper aims to review published evidence …