[HTML][HTML] A systematic literature review on the application of machine-learning models in behavioral assessment of autism spectrum disorder
Autism spectrum disorder (ASD) is associated with significant social, communication, and
behavioral challenges. The insufficient number of trained clinicians coupled with limited …
behavioral challenges. The insufficient number of trained clinicians coupled with limited …
Predicting genetic disorder and types of disorder using chain classifier approach
Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence
which can be developed or inherited from parents. Such mutations may lead to fatal …
which can be developed or inherited from parents. Such mutations may lead to fatal …
Improving the classification of alzheimer's disease using hybrid gene selection pipeline and deep learning
Alzheimer's is a progressive, irreversible, neurodegenerative brain disease. Even with
prominent symptoms, it takes years to notice, decode, and reveal Alzheimer's. However …
prominent symptoms, it takes years to notice, decode, and reveal Alzheimer's. However …
Genomic insights and advanced machine learning: characterizing autism spectrum disorder biomarkers and genetic interactions
Abstract Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition
characterized by altered brain connectivity and function. In this study, we employed …
characterized by altered brain connectivity and function. In this study, we employed …
Bioinformatics investigation on blood-based gene expressions of Alzheimer's disease revealed ORAI2 gene biomarker susceptibility: An explainable artificial …
The progressive, chronic nature of Alzheimer's disease (AD), a form of dementia, defaces
the adulthood of elderly individuals. The pathogenesis of the condition is primarily …
the adulthood of elderly individuals. The pathogenesis of the condition is primarily …
Machine learning approaches for neurological disease prediction: A systematic review
A Fatima, S Masood - Expert Systems, 2024 - Wiley Online Library
In this article, we present a systematic and exhaustive review regarding the trends, datasets
employed, as well as findings achieved in the last 11 years in neurological disorder …
employed, as well as findings achieved in the last 11 years in neurological disorder …
机器学习在自闭症儿童早期识别和诊断领域的应用
侯婷婷， 陈潇， 孔德彭， 邵秀筠， 林丰勋， 李开云 - 心理科学进展, 2022 - journal.psych.ac.cn
早发现, 早诊断, 早干预是开展自闭症儿童教育康复工作的共识, 但传统识别和诊断方法局限及
专业人员缺乏常导致自闭症儿童错失最佳干预期. 为改善现状, 近年来机器学习凭借其客观准确 …
专业人员缺乏常导致自闭症儿童错失最佳干预期. 为改善现状, 近年来机器学习凭借其客观准确 …
AFF-BPL: An Adaptive Feature Fusion Technique for the Diagnosis of Autism Spectrum Disorder using Bat-PSO-LSTM based Framework
Autism spectrum disorder (ASD) is a neurological condition revealed by deficiencies in
physical well-being, social communication, hyperactive behavior, and increased sensitivity …
physical well-being, social communication, hyperactive behavior, and increased sensitivity …
FSF-GA: A Feature Selection Framework for Phenotype Prediction Using Genetic Algorithms
ME Mowlaei, X Shi - Genes, 2023 - mdpi.com
(1) Background: Phenotype prediction is a pivotal task in genetics in order to identify how
genetic factors contribute to phenotypic differences. This field has seen extensive research …
genetic factors contribute to phenotypic differences. This field has seen extensive research …
Machine learning in autism spectrum disorder diagnosis and treatment: Techniques and applications
A Singh, Z Farooqui, B Sattler, E Li, S Nerkar… - … Techniques for Autism …, 2023 - Elsevier
Abstract Machine learning (ML) has become increasingly useful in health care,
demonstrating effectiveness in a wide range of tasks such as diagnosing conditions and …
demonstrating effectiveness in a wide range of tasks such as diagnosing conditions and …