Depresión por COVID-19 en estudiantes universitarios que viven en una zona vulnerable de Lima Norte DOI Creative Commons
Lucía Asencios-Trujillo,

Lida Asencios-Trujillo,

Carlos La Rosa-Longobardi

и другие.

Salud Ciencia y Tecnología - Serie de Conferencias, Год журнала: 2022, Номер 1, С. 91 - 91

Опубликована: Сен. 28, 2022

Al ser Estudiantes Universitarios de zonas vulnerables a nivel socioeconómico, la sintomatología depresiva tiende aumentarse durante pandemia, por lo que el objetivo investigación es determinar depresión en post COVID-19 Lima Norte. Es un estudio cuantitativo, descriptivo, transversal y no experimental, con una población 30 pobladores resolvieron cuestionario aspectos sociodemográficos Escala Autoevaluación para Depresión Zung. En sus resultados, 5 %(n=7) los están deprimidos, 30,5 %(n=10) ligeramente deprimidos 64,5 %(n=13) tienen normal. conclusión, esta permitirá destacar las condiciones desfavorables preexisten nuestro país, además producto pandemia se agravó dando así necesidad hacer intervenciones largo plazo sobre salud mental.

Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection DOI Open Access
Bayode Ogunleye, Hemlata Sharma, Olamilekan Shobayo

и другие.

Опубликована: Июль 16, 2024

The world health organisation (WHO) revealed approximately 280 million people in the suffer from depression. Yet, existing studies on early-stage depression detection using machine learning (ML) techniques are limited. Prior have applied a single stand-alone algorithm which unable to deal with data complexities, prone overfitting and limited generalisation. To this end, our paper examined performance of several ML algorithms for two benchmark social media datasets (D1 D2). More specifically, we incorporated sentiment indicator improve model performance. Our experimental results showed that sentence bidirectional encoder representations transformers (SBERT) numerical vectors fitted into stacking ensemble achieved comparable F1 scores 69% dataset (D1) 76% (D2). findings suggest utilising indicators as additional feature yields an improved thus, recommend development depressive term corpus future work.

Язык: Английский

Процитировано

5

A novel hybrid CNN-KNN ensemble voting classifier for Parkinson’s disease prediction from hand sketching images DOI
Shawki Saleh, Asmae Ouhmida, Bouchaib Cherradi

и другие.

Multimedia Tools and Applications, Год журнала: 2024, Номер unknown

Опубликована: Май 14, 2024

Язык: Английский

Процитировано

3

Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment DOI Creative Commons

Dillan Imans,

Tamer Abuhmed, Meshal Alharbi

и другие.

Diagnostics, Год журнала: 2024, Номер 14(21), С. 2385 - 2385

Опубликована: Окт. 25, 2024

Depression is a pervasive mental health condition, particularly affecting older adults, where early detection and intervention are essential to mitigate its impact. This study presents an explainable multi-layer dynamic ensemble framework designed detect depression assess severity, aiming improve diagnostic precision provide insights into contributing factors.

Язык: Английский

Процитировано

2

A Multifaceted Approach to Understanding Mental Health Crises in the COVID-19 Era DOI
Sayak Das,

Somashri Pal Kar,

Soumyadeep Sil

и другие.

Advances in medical diagnosis, treatment, and care (AMDTC) book series, Год журнала: 2024, Номер unknown, С. 97 - 119

Опубликована: Янв. 5, 2024

The COVID-19 pandemic, starting in Wuhan, China December 2019, led to widespread health and economic challenges, causing millions of deaths globally. Beyond physical health, it triggered a mental crisis, especially during lockdowns. To understand address this, study collected data using 90 features the lockdown period. Machine learning (ML) was employed detect key impacting crises. Three ML algorithms—random forest, random tree, multilayer perceptron—were chosen. Random known for robustness, achieved 97.58% accuracy. supervised algorithm with decision trees, yielded 93.24% Multilayer perceptron (MLP), an artificial neural network, 94.20% accuracy by nonlinear relationships. A 10-fold cross-validation method used evaluate these models, enhancing performance reducing bias overfitting. It involves dividing into ten subsets, training on nine, evaluating remaining, repeating this times estimate true unseen data.

Язык: Английский

Процитировано

1

Sentiment Informed Sentence BERT-Ensemble Algorithm for Depression Detection DOI Creative Commons
Bayode Ogunleye, Hemlata Sharma, Olamilekan Shobayo

и другие.

Big Data and Cognitive Computing, Год журнала: 2024, Номер 8(9), С. 112 - 112

Опубликована: Сен. 5, 2024

The World Health Organisation (WHO) revealed approximately 280 million people in the world suffer from depression. Yet, existing studies on early-stage depression detection using machine learning (ML) techniques are limited. Prior have applied a single stand-alone algorithm, which is unable to deal with data complexities, prone overfitting, and limited generalization. To this end, our paper examined performance of several ML algorithms for two benchmark social media datasets (D1 D2). More specifically, we incorporated sentiment indicators improve model performance. Our experimental results showed that sentence bidirectional encoder representations transformers (SBERT) numerical vectors fitted into stacking ensemble achieved comparable F1 scores 69% dataset (D1) 76% (D2). findings suggest utilizing as an additional feature yields improved performance, thus, recommend development depressive term corpus future work.

Язык: Английский

Процитировано

1

Predicting Depression and Anxiety: A Multi-Layer Perceptron for Analyzing the Mental Health Impact of COVID-19 DOI

D. D. Fong,

Tianshu Chu,

Matthew Heflin

и другие.

Опубликована: Май 17, 2024

Язык: Английский

Процитировано

1

Early Risk Prediction of Depression Based on Social Media Posts in Arabic DOI

Kefaya Sabaneh,

Momen Abu Salameh,

Fatima Khaleel

и другие.

Опубликована: Ноя. 6, 2023

Depression is a prevalent global health issue, impacting various aspects of individuals' lives, including home and social interactions. In the Arabic environment, stigma surrounding mental disorders limited awareness in psychiatry domain has made early diagnosis depression challenging task. However, media platforms have enabled individuals to express their thoughts personal experiences, making these valuable resource for monitoring. this paper, we propose an approach predict signs utilizing posts expressed on Twitter platform. The proposed methodology integrates knowledge extracted using LLM-based transformer, UMLS medical resource, machine learning prediction algorithms. To best our knowledge, first research study that maps translated texts external resources improve accuracy model. model consists four phases. Firstly, NLP-based data preprocessing pipeline employed ensure input dataset suitable format analysis. Secondly, ChatGPT transformer utilized translate tweets into English, enabling further processing analysis English. Thirdly, relevant concepts are from text quickUMLS tool metathesaurus, aiding identifying important terms related health. Fourthly, TF-IDF Bag Words (BOW) algorithms used assign weights features, highlighting significance concepts. Finally, classification algorithms, Support Vector Machine (SVM), Logistic Regression (LR), Random Forest (RF), Naive Bayes (NB), Stochastic Gradient Descent (SGD), trained Among classifiers, with demonstrated performance, achieving 80.24%.

Язык: Английский

Процитировано

2

A Review of Mental Health Analysis Through Social Media Using Machine Learning and Deep Learning Approaches DOI

Maryam Saleem,

Hammad Afzal

Опубликована: Май 23, 2024

Язык: Английский

Процитировано

0

Sentiment Analysis Based on Machine Learning Techniques: A Comprehensive Review DOI Open Access

Ari Ibrahim Hamid,

Adnan Mohsin Abdulazeez

Indonesian Journal of Computer Science, Год журнала: 2024, Номер 13(3)

Опубликована: Июнь 15, 2024

In the landscape of digital communication, sentiment analysis stands out as a pivotal technology for deciphering vast troves unstructured text generated online. When integrated with machine learning, transforms into powerful tool capable distilling insights from complex human emotions and opinions expressed across social media, reviews, forums. This review paper embarks on thorough exploration integration learning techniques analysis, shedding light latest advancements, challenges, applications spanning various sectors including public health, finance, consumer behavior. It meticulously examines role in elevating through improved accuracy, adaptability, depth analysis. Furthermore, discusses implications these technologies understanding sentiment, tracking health trends, forecasting market movements. By synthesizing findings seminal studies cutting-edge research, this not only charts current but also forecasts trajectory underscores necessity ongoing innovation models to keep pace evolving discourse. The presented herein aim guide future research endeavors, highlight transformative impact outline potential new that could benefit society at large.

Язык: Английский

Процитировано

0

A Machine Learning based Depression Detection via Questionnaires and Video DOI
Vijay P. Singh, Vineet Gupta, V. K. Tripathi

и другие.

2022 13th International Conference on Computing Communication and Networking Technologies (ICCCNT), Год журнала: 2024, Номер unknown, С. 1 - 7

Опубликована: Июнь 24, 2024

Язык: Английский

Процитировано

0