A Mobile Application for the Early Detection of ADHD Using Random Forest: The Case Study of Primary School Students in Peru

Jose Vara; Djalma Dioses; Lenis Wong

doi:10.48084/etasr.17403

Authors

Jose Vara Peruvian University of Applied Sciences (UPC), Lima, Peru
Djalma Dioses Peruvian University of Applied Sciences (UPC), Lima, Peru
Lenis Wong Peruvian University of Applied Sciences (UPC), Lima, Peru

Volume: 16 | Issue: 3 | Pages: 36071-36080 | June 2026 | https://doi.org/10.48084/etasr.17403

Received: 8 January 2026 | Revised: 20 February 2026, 12 March 2026, 16 March 2026, and 25 March 2026 | Accepted: 27 March 2026 | Online: 6 June 2026

Corresponding author: Lenis Wong

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is often underdiagnosed in schools due to the need for lengthy clinical assessments and a reduced number of specialists. Therefore, many children are not identified in time to receive support, especially in resource-scarce educational environments. This study developed a mobile application for the early screening of ADHD in primary school students, based on a Random Forest (RF) Machine Learning (ML) model trained with school-based data. The proposal was conducted in two stages: adaptation of the predictive model to the educational context and construction of the application with modules for digital questionnaires, longitudinal monitoring of each student, and visualization of the assessment results. To validate the proposal, the application was tested in a real school context and compared with the traditional clinical assessment method, where three main indicators were measured: time per student, completion rate of evaluations, and positive predictive value. The application reduced the average assessment time from 12 min to 4 min per student, maintained 100% completion rate of evaluations, and obtained a 100% positive predictive value. Overall, these results support the proposed application as a viable, effective, and reliable alternative for early ADHD screening in a school setting.

Keywords:

Attention Deficit Hyperactivity Disorder (ADHD), Machine Learning (ML), Random Forest (RF), educational environment, behavioral data

References

Instituto Nacional de Salud del Niño - Breña, "Guía técnica para el diagnóstico y tratamiento del trastorno de hiperactividad y déficit de atención," Resolución Directoral N.° 058-2023-INSN-DG, 2023. [Online]. Available: https://www.gob.pe/institucion/insn/normas-legales/5497709-058-2023-insn-dg.

C.-M. Yang, J. Shin, J. I. Kim, Y. B. Lim, S. H. Park, and B.-N. Kim, "Classifying Children with ADHD Based on Prefrontal Functional Near-infrared Spectroscopy Using Machine Learning," vol. 21, no. 4, pp. 693–700, Nov. 2023.

X. Meng et al., "Diagnostic model optimization method for ADHD based on brain network analysis of resting-state fMRI images and transfer learning neural network," Frontiers in Human Neuroscience, vol. 16, Oct. 2022, Art. no. 1005425.

M. Firouzi, K. Kazemi, M. Ahmadi, M. S. Helfroush, and A. Aarabi, "Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis," Scientific Reports, vol. 14, no. 1, Oct. 2024, Art. no. 24473.

O. Karabiber Cura, A. Akan, and S. Kocaaslan Atli, "Detection of Attention Deficit Hyperactivity Disorder based on EEG feature maps and deep learning," Biocybernetics and Biomedical Engineering, vol. 44, no. 3, pp. 450–460, July 2024.

E. Ahmadi Moghadam, F. Abedinzadeh Torghabeh, S. A. Hosseini, and M. H. Moattar, "Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value," Neuroinformatics, vol. 22, no. 4, pp. 521–537, Oct. 2024.

D. Andrikopoulos, G. Vassiliou, P. Fatouros, C. Tsirmpas, A. Pehlivanidis, and C. Papageorgiou, "Machine learning-enabled detection of attention-deficit/hyperactivity disorder with multimodal physiological data: a case-control study," BMC Psychiatry, vol. 24, no. 1, Aug. 2024, Art. no. 547.

J. W. Kim, B.-N. Kim, J. I. Kim, C.-M. Yang, and J. Kwon, "Electroencephalogram (EEG) Based Prediction of Attention Deficit Hyperactivity Disorder (ADHD) Using Machine Learning," Neuropsychiatric Disease and Treatment, vol. 21, pp. 271–279, Feb. 2025.

H. Choi et al., "Retinal fundus imaging as biomarker for ADHD using machine learning for screening and visual attention stratification," npj Digital Medicine, vol. 8, no. 1, Mar. 2025, Art. no. 164.

J. H. Yoo et al., "Development of an innovative approach using portable eye tracking to assist ADHD screening: a machine learning study," Frontiers in Psychiatry, vol. 15, Feb. 2024, Art. no. 1337595.

Z. Liu et al., "Auxiliary Diagnosis of Children With Attention-Deficit/Hyperactivity Disorder Using Eye-Tracking and Digital Biomarkers: Case-Control Study," JMIR mHealth and uHealth, vol. 12, no. 1, Nov. 2024, Art. no. e58927.

D. Y. Lee et al., "Use of eye tracking to improve the identification of attention-deficit/hyperactivity disorder in children," Scientific Reports, vol. 13, no. 1, Sept. 2023, Art. no. 14469.

H. Qin et al., "Interpretable machine learning approaches for children’s ADHD detection using clinical assessment data: an online web application deployment," BMC Psychiatry, vol. 25, no. 1, Feb. 2025, Art. no. 139.

L. Ter-Minassian et al., "Assessing machine learning for fair prediction of ADHD in school pupils using a retrospective cohort study of linked education and healthcare data," BMJ Open, vol. 12, Dec. 2022, Art. no. e058058.

S. Altun, A. Alkan, and H. Altun, "Automatic Diagnosis of Attention Deficit Hyperactivity Disorder with Continuous Wavelet Transform and Convolutional Neural Network," vol. 20, no. 4, pp. 715–724, Nov. 2022.

X. Li et al., "Identifying neuroimaging biomarkers of attention-deficit hyperactivity disorder (ADHD) from cortical hemodynamic responses during Go/NoGo task using machine learning approaches," Progress in Neuro-Psychopharmacology and Biological Psychiatry, vol. 140, July 2025, Art. no. 111417.

J. S. Martin, W. Romero, J. L. Castillo-Sequera, and L. Wong, "Talki: A Mobile Application to Improve English Learning of High School Students in Peru utilizing Virtual Reality and Gamification," Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 17472–17481, Oct. 2024.

J. Vara, D. Dioses, and L. Wong, "Machine Learning Models for the Detection of ADHD in Primary School Students in Peru," in 2025 38th Conference of Open Innovations Association (FRUCT), Helsinki, Finland, 2025, pp. 309–317.