Book ChapterDOI
Optimal Boosting Label Weighting Extreme Learning Machine for Mental Disorder Prediction and Classification
E. Laxmi Lydia
- pp 1-15
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The article was published on 2022-01-01. It has received 0 citations till now. The article focuses on the topics: Extreme learning machine & Computer science.read more
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Journal ArticleDOI
Machine Learning Approaches for Clinical Psychology and Psychiatry.
TL;DR: The limitations of current statistical paradigms in mental health research are critiqued, and an introduction is provided to critical machine learning methods used in clinical studies to reinforce the usefulness of machine learning Methods and provide evidence of their potential.
Journal ArticleDOI
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning.
TL;DR: A layered, hierarchical model for translating raw sensor data into markers of behaviors and states related to mental health is provided, focused principally on smartphones, but also including studies of wearables, social media, and computers.
Journal ArticleDOI
Machine learning in autistic spectrum disorder behavioral research: A review and ways forward
TL;DR: This article critically analyses recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data.
Journal ArticleDOI
Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review.
Seyedeh Neelufar Payrovnaziri,Zhaoyi Chen,Pablo Rengifo-Moreno,Pablo Rengifo-Moreno,Tim Miller,Jian-Guo Bian,Jonathan H. Chen,Xiuwen Liu,Zhe He +8 more
TL;DR: It is found that XAI evaluation in medicine has not been adequately and formally practiced, andple opportunities exist to advance XAI research in medicine.
Journal ArticleDOI
Predicting Anxiety, Depression and Stress in Modern Life using Machine Learning Algorithms
TL;DR: After applying the different methods, it was found that classes were imbalanced in the confusion matrix and the f1 score measure was added, which helped identify the best accuracy model among the five applied algorithms as the Random Forest classifier.
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