Supervised Machine Learning for a Novel Autism Prediction Tool in Adults DOI Open Access
Sam Brandsen, Shreyas Hallur,

Isabelle Andrews

et al.

Published: Dec. 13, 2024

For many late-identified autistic adults, the realization that they are can be a key first step to accessing supportive resources and accommodations developing self-understanding. We introduce novel screening tool for traits designed primarily by adults. It includes options assess masking, sensory processing differences, commonly co-occurring medical or mental health conditions, questions about social communication differences repetitive behavior. used simple supervised machine learning algorithm generate score predicting whether individual is autistic. Our results indicate this was able distinguish respondents who non-autistic from self-reported being formally diagnosed as Additionally, our remained effective in identifying autism with other under-represented identities. Finally, we separately analyzed responses of self-identified individuals found high degree overlap respondents.

Language: Английский

Depression Detection and Diagnosis Based on Electroencephalogram (EEG) Analysis: A Systematic Review DOI Creative Commons

Kholoud Elnaggar,

M. M. El-Gayar, Mohammed Elmogy

et al.

Diagnostics, Journal Year: 2025, Volume and Issue: 15(2), P. 210 - 210

Published: Jan. 17, 2025

Background: Mental disorders are disturbances of brain functions that cause cognitive, affective, volitional, and behavioral to be disrupted varying degrees. One these is depression, a significant factor contributing the increase in suicide cases worldwide. Consequently, depression has become public health issue globally. Electroencephalogram (EEG) data can utilized diagnose mild disorder (MDD), offering valuable insights into pathophysiological mechanisms underlying mental enhancing understanding MDD. Methods: This survey emphasizes critical role EEG advancing artificial intelligence (AI)-driven approaches for diagnosis. By focusing on studies integrate with machine learning (ML) deep (DL) techniques, we systematically analyze methods utilizing signals identify biomarkers. The highlights advancements preprocessing, feature extraction, model development, showcasing how enhance diagnostic precision, scalability, automation detection. Results: distinguished from prior reviews by addressing their limitations providing researchers future studies. It offers comprehensive comparison ML DL an overview five key steps also presents existing datasets diagnosis critically analyzes limitations. Furthermore, it explores directions challenges, such as robustness augmentation techniques optimizing channel selection improved accuracy. potential transfer encoder-decoder architectures leverage pre-trained models performance discussed. Advancements extraction automated highlighted avenues improving performance. Additionally, integrating Internet Things (IoT) devices continuous monitoring distinguishing between different types identified research areas. Finally, review reliability predictability computational intelligence-based advance Conclusions: study will serve well-organized helpful reference working detecting using provide outlined above, guiding further field.

Language: Английский

Citations

1
Intelligent Internet of Medical Things for Depression: Current Advancements, Challenges, and Trends DOI Creative Commons
Md Belal Bin Heyat, Deepak Adhikari, Faijan Akhtar

et al.

International Journal of Intelligent Systems, Journal Year: 2025, Volume and Issue: 2025(1)

Published: Jan. 1, 2025

We investigated the fusion of Intelligent Internet Medical Things (IIoMT) with depression management, aiming to autonomously identify, monitor, and offer accurate advice without direct professional intervention. Addressing pivotal questions regarding IIoMT’s role in identification, its correlation stress anxiety, impact machine learning (ML) deep (DL) on depressive disorders, challenges potential prospects integrating management IIoMT, this research offers significant contributions. It integrates artificial intelligence (AI) (IoT) paradigms expand studies, highlighting data science modeling’s practical application for intelligent service delivery real‐world settings, emphasizing benefits within IoT. Furthermore, it outlines an IIoMT architecture gathering, analyzing, preempting employing advanced analytics enhance intelligence. The study also identifies current challenges, future trajectories, solutions domain, contributing scientific understanding management. evaluates 168 closely related articles from various databases, including Web Science (WoS) Google Scholar, after rejection repeated books. shows that there is 48% growth articles, mainly focusing symptoms, detection, classification. Similarly, most being conducted United States America, trend increasing other countries around globe. These results suggest essence automated monitoring, suggestions handling depression.

Language: Английский

Citations

1
An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion DOI Creative Commons
Kavita Rawat, Trapti Sharma

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: April 3, 2025

Language: Английский

Citations

0
A Novel Method for Calculating Depression Level Based on Hybrid Neural Networks and Subjective Scales DOI
Zhuozheng Wang,

Keyuan Li,

Xixi Zhao

et al.

Lecture notes in electrical engineering, Journal Year: 2025, Volume and Issue: unknown, P. 292 - 300

Published: Jan. 1, 2025

Language: Английский

Citations

0
Machine learning-based predictive modeling of depressive symptoms in Chinese adolescents DOI
Lijie Ding, Zhiwei Wu,

Qingjian Wu

et al.

Journal of Affective Disorders, Journal Year: 2025, Volume and Issue: unknown, P. 119399 - 119399

Published: May 1, 2025

Language: Английский

Citations

0
Resting-State Electroencephalogram Depression Diagnosis Based on Traditional Machine Learning and Deep Learning: A Comparative Analysis DOI Creative Commons
Haijun Lin, Jing Fang, Junpeng Zhang

et al.

Sensors, Journal Year: 2024, Volume and Issue: 24(21), P. 6815 - 6815

Published: Oct. 23, 2024

The global prevalence of Major Depressive Disorder (MDD) is increasing at an alarming rate, underscoring the urgent need for timely and accurate diagnoses to facilitate effective interventions treatments. Electroencephalography remains a widely used neuroimaging technique in psychiatry, due its non-invasive nature cost-effectiveness. With rise computational integration EEG with artificial intelligence has yielded remarkable results diagnosing depression. This review offers comparative analysis two predominant methodologies research: traditional machine learning deep methods. Furthermore, this addresses key challenges current research suggests potential solutions. These insights aim enhance diagnostic accuracy depression also foster further development area psychiatry.

Language: Английский

Citations

2
Neurocognitive Approach for Assessing Visual Engagement in Neuromarketing DOI
Ahsen Hussain, Syed Aun Muhammad, Usman Saeed

et al.

Published: Oct. 15, 2024

Language: Английский

Citations

0
Supervised Machine Learning for a Novel Autism Prediction Tool in Adults DOI Open Access
Sam Brandsen, Shreyas Hallur,

Isabelle Andrews

et al.

Published: Dec. 13, 2024

For many late-identified autistic adults, the realization that they are can be a key first step to accessing supportive resources and accommodations developing self-understanding. We introduce novel screening tool for traits designed primarily by adults. It includes options assess masking, sensory processing differences, commonly co-occurring medical or mental health conditions, questions about social communication differences repetitive behavior. used simple supervised machine learning algorithm generate score predicting whether individual is autistic. Our results indicate this was able distinguish respondents who non-autistic from self-reported being formally diagnosed as Additionally, our remained effective in identifying autism with other under-represented identities. Finally, we separately analyzed responses of self-identified individuals found high degree overlap respondents.

Language: Английский

Citations

0