ADHD Diagnosis with Machine Learning and Sensors: A Mapping Study
DOI:
https://doi.org/10.32870/recibe.v12i2.331Keywords:
ADHD, Machine Learning, Diagnosis, Attention deficit hyperactivity disorder, SensorsAbstract
Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder whose main characteristics are hyperactivity and lack of concentration in daily activities. It is a disorder known to occur early and affects school performance. The diagnosis of ADHD has been a complex procedure, requiring extensive manual data collection and analysis. For this reason, machine learning (ML) tools have been proposed to improve the accuracy and time of diagnosis. Currently, there is a proliferation of methodologies that recognize different types of patient data for diagnosing ADHD. This study aims to review previous research on this area within the state of the art and answer the questions raised. The article will discuss topics about machine learning models and the different data collected. The results show that neural network models and Support Vector Machine (SVM) are the most frequently used. Furthermore, movement and acceleration data have better performance compared to implementations by neural processes, contrary to what was expected. We can conclude that studies related to the acquisition of motion data have great promise in their implementation and development in future times for greater precision in clinical results.References
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