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Random Forest Feature Selection for Data Coming from Evaluation Sheets of Subjects with ASDs
2016
Proceedings of the 2016 Federated Conference on Computer Science and Information Systems
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We deal with the problem of initial analysis of data coming from evaluation sheets of subjects with Autism Spectrum Disorders (ASDs). ...
The main goal is to use appropriate data to build simpler and more accurate classifiers. The feature selection method based on random forest is used. ...
In the first step of our research, we are interested in initial analysis of data coming from evaluation sheets of subjects with ASDs. ...
doi:10.15439/2016f274
dblp:conf/fedcsis/PancerzPG16
fatcat:vrfmc2q3djgytpv2re2sakg3bi
Attention Measurement of an Autism Spectrum Disorder User Using EEG Signals: A Case Study
2022
Mathematical and Computational Applications
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We train and evaluate several machine learning (ML) models with these features. ...
People with ASD usually have variable attention levels because they have hypersensitivity and large amounts of environmental information are a problem for them. ...
with ASD and 20 subjects diag- EEG data. rithm to extract 794 quantitative features (TSFRESH Python package). ...
doi:10.3390/mca27020021
fatcat:i4yubwnmq5gnfnc5gnljsshv5m
A Review of Machine Learning Methods of Feature Selection and Classification for Autism Spectrum Disorder
2020
Brain Sciences
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Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. ...
This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. ...
This sheet consists of 70 cases where every subject was evaluated using questions grouped by 17 areas with 300 attributes. ...
doi:10.3390/brainsci10120949
pmid:33297436
pmcid:PMC7762227
fatcat:zmoe3mdkh5db5beg7n7yzu2eve
Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review
2019
Review Journal of Autism and Developmental Disorders
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, and statistically sound approaches for mining ASD data. ...
Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions ...
MARA utilizes an ADTree trained on the answer sheets of 891 subjects with ASD and 75 subjects without ASD from the Autism Genetic Resource Exchange (AGRE; Geschwind et al. 2001) database to generate ...
doi:10.1007/s40489-019-00158-x
fatcat:57ef4bohvbfpzbei4nz7q5oheu
A Sensor and Machine Learning-Based Sensory Management Recommendation System for Children with Autism Spectrum Disorders
2022
Sensors
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The sensory management recommendation system could work as an intelligent companion for ASD children that helps with their in-class performance by recommending management strategies in relation to the ...
The evaluation results suggested that the use of the system had a positive impact on children's performance and its design was user-friendly. ...
The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. ...
doi:10.3390/s22155803
pmid:35957356
fatcat:ormsfjucejc2njz46v3ebd5eqa
Identification of Bamboo Species Based on Extreme Gradient Boosting (XGBoost) Using Zhuhai-1 Orbita Hyperspectral Remote Sensing Imagery
2022
Sensors
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The random forest and Spearman's rank correlation analysis were used to sort the main variables that affect classification accuracy and minimize the effects of multicollinearity among variables. ...
Mapping the distribution of bamboo species is vital for the sustainable management of bamboo and for assessing its ecological and socioeconomic value. ...
Acknowledgments: Thanks to Zhengwei He for providing the spectrometer (model: SVC HR-1024i); Yuhao Luo, Chuntao Geng, and Shuai Li for the investigation; Dong Wang and Shaowen Zhang for the visualization ...
doi:10.3390/s22145434
pmid:35891113
pmcid:PMC9315677
fatcat:o4mrrqed2nhkrm54jymzib5wem
Promoting social communication through music therapy in children with autism spectrum disorder
[article]
2015
The PhD Series of the Faculty of the Humanities
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the communicative behavior of children with ASD. ...
Effects of IMT were investigated systematically for the first time in 1994 when Edgerton presented evidence from a study involving eleven children aged 6 to 9 that suggested IMT's effectiveness in increasing ...
Data collection and analysis Two authors independently selected studies, assessed risk of bias, and extracted data from all included studies. ...
doi:10.5278/vbn.phd.hum.00010
fatcat:4b2zcu6cwvcjxkrcdzktcl6ipe
Machine Learning-Based Automatic Utterance Collection Model for Language Development Screening of Children
2022
Applied Sciences
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The strategy we suggest has the potential to reduce the cost of collecting data for evaluating children's language development while maintaining data collection impartiality. ...
It has the benefit of being able to cope with several children, which reduces costs and allows for the collection of objectified utterance data through consistent conversation settings. ...
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app12094747
fatcat:c5qvth7ig5d7nno7jw73so3f7e
ACNP 56th Annual Meeting: Poster Session I, December 4, 2017
2017
Neuropsychopharmacology
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Methods: We recruited a sample of (N = 63) African-American women with T2DM and high rates of lifetime trauma exposure from the primary care and diabetes specialty clinic waiting rooms of Grady Memorial ...
argues for improved integration of care for psychiatric and medical disorders. ...
To investigate the role of mGluR5 in ASD in vivo we carried out a positron emission tomography (PET) study with the mGluR5-selective radiotracer [11C]ABP688 in subjects with ASD and healthy controls. ...
doi:10.1038/npp.2017.264
pmid:29192265
fatcat:psc2w4azgrbfhjtxe6oxrnsdaq
ACNP 55th Annual Meeting: Poster Session III
2016
Neuropsychopharmacology
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Our laboratory recently developed two PET radioligands: 11C-PS13 for COX-1 and 11C-MC1 for COX-2, each of which potently and selectively inhibits the cognate enzyme in whole blood assays from monkey and ...
evaluation of these two radioligands in a healthy rhesus monkey brain. ...
Methods: RNA sequencing data was evaluated for postmortem tissue samples from the dorsolateral prefrontal cortex (DLPFC) of 106 subjects (53 BD subjects and 53 matched control subjects). ...
doi:10.1038/npp.2016.242
fatcat:4efd43swjbboranfrcs3pp32h4
ACNP 58th Annual Meeting: Poster Session III
2019
Neuropsychopharmacology
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This study obtains the approval from Japanese association of Neuro-Psychiatric Clinics Study Ethical Review Board, and the data investigated were retrieved from databases and de-identified before data ...
Methods: Participants were 4,964 youths (ages 5-17 years) from seven international sites, presenting with a wide range of symptom severity (healthy, non-selected, high-risk, or clinicallyanxious youth) ...
Random forest analysis was used to generate baseline microbiome-based classifier for prediction of treatment response. ...
doi:10.1038/s41386-019-0547-9
pmid:31801974
pmcid:PMC6957926
fatcat:dd7d43ysfvc5bbbstfl73szya4
Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases
[chapter]
2022
Comparative Analysis of Different Machine Learning Classifiers for the Prediction of Chronic Diseases
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Precise diagnosis of these diseases on time is very significant for maintaining a healthy life. ...
A comparative study of different machine learning classifiers for chronic disease prediction viz Heart Disease & Diabetes Disease is done in this paper. ...
The region-based segmentation will segment the data dependent on the taken-out features using GLCM algorithm. ...
doi:10.13052/rp-9788770227667
fatcat:da47mjbbyzfwnbpde7rgbrlppe
Abstracts From the 26th Annual Health Care Systems Research Network Conference, April 8–10, 2020
2020
Journal of Patient-Centered Research and Reviews
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At the second phase, 512 reports were randomly selected from all available data spanning January 1, 2017-December 31, 2018, stratified by clinical departments, and manually reviewed by 7 physicians. ...
Background: Down syndrome (DS) is characterized by intellectual disabilities, dysmorphic features, and comorbid conditions that result from increased genetic material of the 21 st chromosome. ...
To balance the trade-off between model simplicity and performance, 23 variables from univariate feature selection evaluated using random forest were selected in predicting HA-AKI (AUC: 0.87). ...
doi:10.17294/2330-0698.1762
fatcat:2kezambzxvcxhnkeofw55isgiu
Mining Electronic Health Records: A Survey
[article]
2017
arXiv
pre-print
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We conclude this survey with a comprehensive summary of clinical data mining applications of EHR data, as illustrated in the online supplement. ...
With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research. ...
[Ishwaran et al. 2008] proposed Random Survival Forests for analyzing right censored survival data. ...
arXiv:1702.03222v2
fatcat:aizt3bnmibcc7kv67h6qf7ts7q
Creating bridges: Music-Oriented Counseling for Parents of children with Autism Spectrum Disorder
[article]
2016
The PhD Series of the Faculty of the Humanities
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Users may download and print one copy of any publication from the public portal for the purpose of private study or research. ? ...
that users recognise and abide by the legal requirements associated with these rights. ? ...
The music therapist watched random videos of parents' sessions -one for each family, and completed the fidelity assessment, (the process of random selection of the videos was explained in the design, section ...
doi:10.5278/vbn.phd.hum.00042
fatcat:ru3biia3pfc6loqqdvy6deffzm
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