Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder. From the data collected by the Ministry of Public Health, Thailand, it has been reported that more than one million Thai youths (6–12 years) have been diagnosed with ADHD (2012–2018) This disorder is more likely to occ...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Children |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9067/10/8/1288 |
| _version_ | 1797585128523825152 |
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| author | Pornsiri Chatpreecha Sasiporn Usanavasin |
| author_facet | Pornsiri Chatpreecha Sasiporn Usanavasin |
| author_sort | Pornsiri Chatpreecha |
| collection | DOAJ |
| description | Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder. From the data collected by the Ministry of Public Health, Thailand, it has been reported that more than one million Thai youths (6–12 years) have been diagnosed with ADHD (2012–2018) This disorder is more likely to occur in males (12%) than females (4.2%). If ADHD goes untreated, there might be problems for individuals in the long run. This research aims to design a collaborative knowledge framework for personalised ADHD treatment recommendations. The first objective is to design a framework and develop a screening tool for doctors, parents, and teachers for observing and recording behavioural symptoms in ADHD children. This screening tool is a combination of doctor-verified criteria and the ADHD standardised screening tool (Vanderbilt). The second objective is to introduce practical algorithms for classifying ADHD types and recommending appropriate individual behavioural therapies and activities. We applied and compared four well-known machine-learning methods for classifying ADHD types. The four algorithms include Decision Tree, Naïve Bayes, neural network, and k-nearest neighbour. Based on this experiment, the Decision Tree algorithm yielded the highest average accuracy, which was 99.60%, with F1 scores equal to or greater than 97% for classifying each type of ADHD. |
| first_indexed | 2024-03-11T00:02:33Z |
| format | Article |
| id | doaj.art-a026659a50d04760a9aa21ed9170592a |
| institution | Directory Open Access Journal |
| issn | 2227-9067 |
| language | English |
| last_indexed | 2024-03-11T00:02:33Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Children |
| spelling | doaj.art-a026659a50d04760a9aa21ed9170592a2023-11-19T00:39:11ZengMDPI AGChildren2227-90672023-07-01108128810.3390/children10081288Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) TreatmentsPornsiri Chatpreecha0Sasiporn Usanavasin1School of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, ThailandSchool of Information, Computer and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12000, ThailandAttention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder. From the data collected by the Ministry of Public Health, Thailand, it has been reported that more than one million Thai youths (6–12 years) have been diagnosed with ADHD (2012–2018) This disorder is more likely to occur in males (12%) than females (4.2%). If ADHD goes untreated, there might be problems for individuals in the long run. This research aims to design a collaborative knowledge framework for personalised ADHD treatment recommendations. The first objective is to design a framework and develop a screening tool for doctors, parents, and teachers for observing and recording behavioural symptoms in ADHD children. This screening tool is a combination of doctor-verified criteria and the ADHD standardised screening tool (Vanderbilt). The second objective is to introduce practical algorithms for classifying ADHD types and recommending appropriate individual behavioural therapies and activities. We applied and compared four well-known machine-learning methods for classifying ADHD types. The four algorithms include Decision Tree, Naïve Bayes, neural network, and k-nearest neighbour. Based on this experiment, the Decision Tree algorithm yielded the highest average accuracy, which was 99.60%, with F1 scores equal to or greater than 97% for classifying each type of ADHD.https://www.mdpi.com/2227-9067/10/8/1288attention deficit hyperactivity disordermachine learningknowledge frameworkscreening tool |
| spellingShingle | Pornsiri Chatpreecha Sasiporn Usanavasin Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments Children attention deficit hyperactivity disorder machine learning knowledge framework screening tool |
| title | Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments |
| title_full | Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments |
| title_fullStr | Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments |
| title_full_unstemmed | Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments |
| title_short | Design of a Collaborative Knowledge Framework for Personalised Attention Deficit Hyperactivity Disorder (ADHD) Treatments |
| title_sort | design of a collaborative knowledge framework for personalised attention deficit hyperactivity disorder adhd treatments |
| topic | attention deficit hyperactivity disorder machine learning knowledge framework screening tool |
| url | https://www.mdpi.com/2227-9067/10/8/1288 |
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