Machine learning in mental health: a scoping review of methods and applications
BackgroundThis paper aims to synthesise the literature on machine learning (ML) and big
data applications for mental health, highlighting current research and applications in …
data applications for mental health, highlighting current research and applications in …
Disease prediction by machine learning over big data from healthcare communities
With big data growth in biomedical and healthcare communities, accurate analysis of
medical data benefits early disease detection, patient care, and community services …
medical data benefits early disease detection, patient care, and community services …
Patient similarity: emerging concepts in systems and precision medicine
SA Brown - Frontiers in physiology, 2016 - frontiersin.org
Healthcare data generates a huge volume of information in various formats at high velocity
with sometimes questionable veracity (Barkhordari and Niamanesh, 2015)(4V). As a result …
with sometimes questionable veracity (Barkhordari and Niamanesh, 2015)(4V). As a result …
Designing disease prediction model using machine learning approach
D Dahiwade, G Patle, E Meshram - 2019 3rd International …, 2019 - ieeexplore.ieee.org
Now-a-days, people face various diseases due to the environmental condition and their
living habits. So the prediction of disease at earlier stage becomes important task. But the …
living habits. So the prediction of disease at earlier stage becomes important task. But the …
Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory study
Autism spectrum is a brain development condition that impairs an individual's capacity to
communicate socially and manifests through strict routines and obsessive–compulsive …
communicate socially and manifests through strict routines and obsessive–compulsive …
Nanotechnology‐Based Sensitive Biosensors for COVID‐19 Prediction Using Fuzzy Logic Control
V Maheshwari, MR Mahmood… - Journal of …, 2021 - Wiley Online Library
Increasing the growth of big data, particularly in healthcare‐Internet of Things (IoT) and
biomedical classes, tends to help patients by identifying the disease early through methods …
biomedical classes, tends to help patients by identifying the disease early through methods …
Disease risk prediction by using convolutional neural network
S Ambekar, R Phalnikar - 2018 Fourth international conference …, 2018 - ieeexplore.ieee.org
Data analysis plays a significant role in handling a large amount of data in the healthcare.
The previous medical researches based on handling and assimilate a huge amount of …
The previous medical researches based on handling and assimilate a huge amount of …
Medical disease prediction using grey wolf optimization and auto encoder based recurrent neural network
Big data development in biomedical and medical service networks provides a research on
medical data benefits, early ailment detection, patient care and network administrations. e …
medical data benefits, early ailment detection, patient care and network administrations. e …
Unsupervised feature selection via local structure learning and sparse learning
Feature self-representation has become the backbone of unsupervised feature selection,
since it is almost insensitive to noise data. However, feature selection methods based on …
since it is almost insensitive to noise data. However, feature selection methods based on …
A survey on cost types, interaction schemes, and annotator performance models in selection algorithms for active learning in classification
Pool-based active learning (AL) aims to optimize the annotation process (ie, labeling) as the
acquisition of annotations is often time-consuming and therefore expensive. For this …
acquisition of annotations is often time-consuming and therefore expensive. For this …