The Unquiet Mind: ADHD and The Feminine Experience

DOI

10.2991/978-94-6463-978-0_55How to use a DOI?

Keywords

Attention Deficit Hyperactivity Disorder (ADHD); Machine Learning (ML); Deep Learning (DL); Random Forest; Logistic Regression

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a common neurodevelopmental condition that tends to be unnoticed, particularly in adults, mostly women. The lack of diagnosis is due to the fact that the adult symptomatology remains unnoticed or is misconstrued, and adults with ADHD usually present inattentive symptoms instead of overt hyperactivity. Traditional approaches like questionnaires and interviewed methodologies hardly identify mild symptoms since ADHD overlaps many of the medical tests; for instance, brain symptoms/behaviors overlap with depression/anxiety so much. The ADHD symptoms encourage the search for years without being diagnosed, indirectly influencing brain development and well-being in the long run. The present study sought to examine the ability of machine learning as a more precise approach to evaluate the diagnosis. The experiment was conducted with different models such as KNN and Random forest upon a heterogeneous data that we designed which aggregated multiple types of diagnostic tools. The tools were in the form of brain MRI scans, other physiological data, tests of performance, behavior questionnaires, and motion tracking. The attempt was made to integrate adequate information that was multifaceted and to apply the capabilities of AI in providing a proper diagnostic tool in ADHD. Upon training and testing of the modesl, it was noted that random forest was the most precise model registering 87.2%. Generally, the outcomes indicate that AI can provide assistance to traditional rating of ADHD. AI can provide a more precise and datadriven diagnosis process for ADHD. With time, AI technology can be incorporated into current diagnosis systems to enhance them to become more comprehensive and effective for people who cannot be diagnosed using current protocols.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Samarth Narvekar
AU  - Manaswi Pitake
AU  - Sakshi Galatage
AU  - Vaibhav Deopa
AU  - Rajashri Khanai
AU  - Salma Shahapur
PY  - 2025
DA  - 2025/12/31
TI  - The Unquiet Mind: ADHD and The Feminine Experience
BT  - Proceedings of the 1st Engineering Data Analytics and Management Conference (EAMCON 2025)
PB  - Atlantis Press
SP  - 649
EP  - 663
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-978-0_55
DO  - 10.2991/978-94-6463-978-0_55
ID  - Narvekar2025
ER  -