DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography
Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is often challenging due to subjective assessments and symptom variability, which can delay accurate detection and treatment. To address these limitations, this study introduces DETEC-ADHD, a web-based application that combines machine learni...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Big Data and Cognitive Computing |
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| Online Access: | https://www.mdpi.com/2504-2289/9/1/3 |
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| author | Ismael Santarrosa-López Giner Alor-Hernández Maritza Bustos-López Jonathan Hernández-Capistrán Laura Nely Sánchez-Morales José Luis Sánchez-Cervantes Humberto Marín-Vega |
| author_facet | Ismael Santarrosa-López Giner Alor-Hernández Maritza Bustos-López Jonathan Hernández-Capistrán Laura Nely Sánchez-Morales José Luis Sánchez-Cervantes Humberto Marín-Vega |
| author_sort | Ismael Santarrosa-López |
| collection | DOAJ |
| description | Attention Deficit Hyperactivity Disorder (ADHD) diagnosis is often challenging due to subjective assessments and symptom variability, which can delay accurate detection and treatment. To address these limitations, this study introduces DETEC-ADHD, a web-based application that combines machine learning (ML) techniques with multi-source data to enhance diagnostic accuracy. Unlike traditional approaches, DETEC-ADHD primarily utilizes extensive personal, medical, and psychological information for its initial classification. DETEC-ADHD further refines diagnoses by identifying ADHD subtypes (inattentive, hyperactive, combined) through theta/beta wave ratio analysis from EEG data, offering neurophysiological insights that complement its classification process. Logistic Regression, selected for its validated accuracy and reliability, served as the ML model for the app. The case studies demonstrated DETEC-ADHD’s effectiveness, achieving 100% accuracy in children and 90% in adults. By integrating diverse data sources with real-time EEG analysis, DETEC-ADHD provides a scalable, cost-effective, and accessible solution for ADHD detection and subtype identification, addressing diagnostic challenges and supporting healthcare providers, particularly in resource-limited environments. |
| format | Article |
| id | doaj-art-f6b1a6c7f005487b9d4eac3a22981634 |
| institution | Kabale University |
| issn | 2504-2289 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Big Data and Cognitive Computing |
| spelling | doaj-art-f6b1a6c7f005487b9d4eac3a229816342025-01-24T13:22:31ZengMDPI AGBig Data and Cognitive Computing2504-22892024-12-0191310.3390/bdcc9010003DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and ElectroencephalographyIsmael Santarrosa-López0Giner Alor-Hernández1Maritza Bustos-López2Jonathan Hernández-Capistrán3Laura Nely Sánchez-Morales4José Luis Sánchez-Cervantes5Humberto Marín-Vega6Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9, No. 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, MexicoTecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9, No. 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, MexicoTecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9, No. 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, MexicoTecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9, No. 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, MexicoCONACYT-Tecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9, No. 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, MexicoTecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9, No. 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, MexicoTecnológico Nacional de México/I. T. Orizaba, Av. Oriente 9, No. 852, Col. Emiliano Zapata, Orizaba C.P. 94320, Veracruz, MexicoAttention Deficit Hyperactivity Disorder (ADHD) diagnosis is often challenging due to subjective assessments and symptom variability, which can delay accurate detection and treatment. To address these limitations, this study introduces DETEC-ADHD, a web-based application that combines machine learning (ML) techniques with multi-source data to enhance diagnostic accuracy. Unlike traditional approaches, DETEC-ADHD primarily utilizes extensive personal, medical, and psychological information for its initial classification. DETEC-ADHD further refines diagnoses by identifying ADHD subtypes (inattentive, hyperactive, combined) through theta/beta wave ratio analysis from EEG data, offering neurophysiological insights that complement its classification process. Logistic Regression, selected for its validated accuracy and reliability, served as the ML model for the app. The case studies demonstrated DETEC-ADHD’s effectiveness, achieving 100% accuracy in children and 90% in adults. By integrating diverse data sources with real-time EEG analysis, DETEC-ADHD provides a scalable, cost-effective, and accessible solution for ADHD detection and subtype identification, addressing diagnostic challenges and supporting healthcare providers, particularly in resource-limited environments.https://www.mdpi.com/2504-2289/9/1/3ADHDEEGmachine learningneurofeedbackwearables |
| spellingShingle | Ismael Santarrosa-López Giner Alor-Hernández Maritza Bustos-López Jonathan Hernández-Capistrán Laura Nely Sánchez-Morales José Luis Sánchez-Cervantes Humberto Marín-Vega DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography Big Data and Cognitive Computing ADHD EEG machine learning neurofeedback wearables |
| title | DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography |
| title_full | DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography |
| title_fullStr | DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography |
| title_full_unstemmed | DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography |
| title_short | DETEC-ADHD: A Data-Driven Web App for Early ADHD Detection Using Machine Learning and Electroencephalography |
| title_sort | detec adhd a data driven web app for early adhd detection using machine learning and electroencephalography |
| topic | ADHD EEG machine learning neurofeedback wearables |
| url | https://www.mdpi.com/2504-2289/9/1/3 |
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