The recent advancements in information technology and bioinformatics have led to
exceptional contributions in medical sciences. Extensive developments have been recorded …

Introduction Machine learning research into automated dementia diagnosis is becoming
increasingly popular but so far has had limited clinical impact. A key challenge is building …

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 …

Background Better understanding and prediction of progression of Parkinson's disease
could improve disease management and clinical trial design. We aimed to use longitudinal …

Cognitive decline is one of the most frequent and disabling nonmotor features of Parkinson's
disease. Around 30% of patients with Parkinson's disease experience mild cognitive …

Background Parkinson's Disease is the second most common neurological disease in over
60s. Cognitive impairment is a major clinical symptom, with risk of severe dysfunction up to …

Bayesian networks are a type of probabilistic graphical models lie at the intersection
between statistics and machine learning. They have been shown to be powerful tools to …

Recent decade, Parkinson's disease (PD), which impairs the life quality for millions of older
people worldwide, has quickly emerged as a serious condition affecting the brain and spinal …

In recent years, many neuroimaging studies have applied artificial intelligence (AI) to
facilitate existing challenges in Parkinson's disease (PD) diagnosis, prognosis, and …

With advancements in information and communication technology, there is a steep increase
in the remote healthcare applications in which patients can get treatment from the remote …