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Automatic Diagnosis of Attention Deficit Hyperactivity Disorder with Continuous Wavelet Transform and Convolutional
Sinan Altun1, Ahmet Alkan1, Hatice Altun2
1Department of Electrical and Electronics Engineering, Kahramanmaras Sutcu Imam University, Kahramanmaras, Turkey.
Insights
This study introduces a novel machine learning approach for diagnosing attention deficit hyperactivity disorder (ADHD) using temperament characteristics. The developed system achieved high classification success, paving the way for AI-driven ADHD diagnostic tools.
Area of Science:
- Neuroscience
- Psychiatry
- Artificial Intelligence
Background:
- Attention deficit hyperactivity disorder (ADHD) significantly impacts a child's education and social development.
- Previous research has established a link between temperament traits and ADHD.
- No prior machine learning studies have focused on diagnosing ADHD using a dataset derived from temperament characteristics.
Purpose of the Study:
- To evaluate the efficacy of a semi-automatic expert decision support system for ADHD diagnosis.
- To investigate the diagnostic potential of temperament characteristics in ADHD.
- To establish a foundation for AI-based ADHD diagnostic systems.
Main Methods:
- Utilized deep learning models for classification.
- Employed Continuous Wavelet Transform to generate signal images from the dataset.
- Developed a semi-automatic expert decision support system.
Main Results:
- The Squeeze Net model demonstrated the highest classification success at 88.33%.
- The study successfully created a dataset based on temperament characteristics for ADHD analysis.
- The findings highlight the potential of temperament traits in ADHD diagnosis.
Conclusions:
- An artificial intelligence-based automatic system for ADHD diagnosis is feasible.
- The study underscores the relationship between temperament characteristics and ADHD diagnosis.
- The developed model offers a novel approach for ADHD assessment.
Objective:
The attention deficit hyperactivity disorder has a negative impact on the child's educational life and relationships with the social environment during childhood and adolescence. The connection between temperament traits and The attention deficit hyperactivity disorder has been proven by various studies. As far as we know, there is no machine learning study to diagnose. The attention deficit hyperactivity disorder in a dataset created using temperament characteristics.
Methods:
Machine learning-based semi-automatic/fully automatic expert decision support systems are frequently used for the diagnosis of various diseases. In this study, it was aimed to reveal the success of a semi-automatic expert decision support system in the diagnosis of attention deficit hyperactivity disorder by using temperament characteristics. The high classification success achieved is a resource for a potential diagnosis of attention deficit hyperactivity disorder expert decision support system. In this respect, this study includes original qualities and innovations.
Results:
Many different deep learning methods were used in the research. Deep learning methods are models that achieve high success by using a large number of images in various image processing competitions. The images of the signals in the data set were first obtained by Continuous Wavelet Transform. The highest classification success in our data set was obtained with the Squeeze Net model with 88.33%.
Conclusion:
The model we propose shows that an automatic system based on artificial intelligence can be created, as well as revealing the relationship between temperament characteristics in the diagnosis of attention deficit hyperactivity in the data set we created.
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