Dear Colleagues,
My name is nominated to be a Topical Editor in Frontiers in Psychiatry journal which is Q2 Web of Science Psychiatry Journal and Frontiers in Neuroinformatic. The main topic of this special issue is Machine Learning methods for Autism Diagnosis problems.
I can give some ideas for that, since I am an Assistant Professor in the Computer Science Department, Faculty of Science, Minia University. The helping ideas as follows:
Comparing Ensemble methods for Autism Diagnosis, Since there is no research accomplished according to this research gap. In addition, every Automated Machine Learning package does not have the capability to compare all Ensemble methods together.
My research paper with my M.Sc. student compared traditional machine, transfer learning with only one package for Automated Machine Learning, while there are a number of Automated machine learning packages with various parameter tuning and various algorithms and there is no comparison for them on Autism Diagnosis.
The recent AI recent trend is Explainable and Interpretable machine learning for medical diagnosis problems
I can help you by research books for these ideas.
The theme of this special issue is
Improving Autism Spectrum Disorder Diagnosis Using Machine Learning Techniques
The goal of this Research Topic is to advance the field of ASD diagnosis by improving the accuracy, efficiency, and accessibility of machine learning techniques. We aim to address the challenges faced in utilizing sMRI and rsFC data for ASD diagnosis and explore novel approaches to enhance the diagnostic process. By integrating multidimensional data and refining machine learning algorithms, we strive for better diagnostic accuracy, early identification, and personalized treatment planning for individuals with ASD.
This Research Topic welcomes contributions that focus on, but are not limited to, the following themes:
Novel machine learning algorithms and techniques for ASD diagnosis
Integration of multimodal data (sMRI, rsFC, genetic information, etc.) for enhanced diagnostic accuracy
Development of interpretable machine learning models for clinical decision support
Identification and validation of robust biomarkers for ASD diagnosis
Exploration of large-scale datasets to improve machine learning models
Standardization and reproducibility in machine learning approaches for ASD diagnosis
We encourage authors to submit original research articles, reviews, opinion papers, and methodological studies that contribute to the advancement of ASD diagnosis using machine learning techniques.
Wow complex yet interesting read. This approach will address the neeed for accurate, effective and efficcient ASD diagnosis hopefully early enough for the affected persons to receive the necessary intervention
Very interesting. Is there any official information regarding of this special issues!
https://www.frontiersin.org/research-topics/59488/improving-autism-spectrum-disorder-diagnosis-using-machine-learning-techniques
The research limitations for my paper in Autism Diagnosis problem are:
1) we used only one packages of AutoML
2) We did not compare between Ensemble methods together and usually, you will find each AutoML package does not include all Ensemble methods for comparative studies.
3) There is not feature selection methods based on some machine learning methods such as Ensemble methods for Autism Diagnosis from images
3) recently there is an Explainable AI paper cited us. Our paper is:
https://onlinelibrary.wiley.com/doi/full/10.4218/etrij.2021-0097
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