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Original Research
Online Published: 07 Jul 2021
 


Application of machine learning in predicting Attention Deficit Hyperactive Disorder (ADHD) in school going children of Pakistan

Salman Mansoor, Shoab Saadat, Sarah Noaman, Hamza Hassan Khan, Salman Assad..


Abstract
Aims:
The purpose of this study was to use machine learning algorithms to predict the probability of a child to have a certain attention deficit hyperactive disorder (ADHD) score under a given set of conditions.

Methods:
This was a cross-sectional survey which employed non-probability convenient sampling technique conducted at two schools in Islamabad, Pakistan. Using the latest version of Konstanz Information Miner (KNIME) Analytics, several machine learning algorithms were tested.

Results:
The area under the curve (AUC) for classification tree was 60.8% with a precision of 75.6% for the prediction of an ADHD score of 20 or more and the probability of 21.3% for a child to have an ADHD score of 20 or more. Important variables associated with a higher ADHD score included father’s profession, school of the child, and the class of child.

Conclusion:
This study shows that machine learning approach may be useful in developing a robust predictive model. Use of predictive model may allow use of limited resources towards assessment of children with higher probability of ADHD.

Key words: Attention deficit hyperactivity disorder (ADHD), Pakistan, Behavior rating scales, Machine learning approach


 
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How to Cite this Article
Pubmed Style

Mansoor S, Saadat S, Noaman S, Khan HH, Assad. S. Application of machine learning in predicting Attention Deficit Hyperactive Disorder (ADHD) in school going children of Pakistan. SAJEM. 2021; 4(1): 18-21. doi:10.5455/sajem.040104

Web Style

Mansoor S, Saadat S, Noaman S, Khan HH, Assad. S. Application of machine learning in predicting Attention Deficit Hyperactive Disorder (ADHD) in school going children of Pakistan. https://www.sajem.org/?mno=75664 [Access: September 13, 2024]. doi:10.5455/sajem.040104

AMA (American Medical Association) Style

Mansoor S, Saadat S, Noaman S, Khan HH, Assad. S. Application of machine learning in predicting Attention Deficit Hyperactive Disorder (ADHD) in school going children of Pakistan. SAJEM. 2021; 4(1): 18-21. doi:10.5455/sajem.040104


Vancouver/ICMJE Style

Mansoor S, Saadat S, Noaman S, Khan HH, Assad. S. Application of machine learning in predicting Attention Deficit Hyperactive Disorder (ADHD) in school going children of Pakistan. SAJEM. (2021), [cited September 13, 2024]; 4(1): 18-21. doi:10.5455/sajem.040104


Harvard Style

Mansoor, S., Saadat, . S., Noaman, . S., Khan, . H. H. & Assad., . S. (2021) Application of machine learning in predicting Attention Deficit Hyperactive Disorder (ADHD) in school going children of Pakistan. SAJEM, 4 (1), 18-21. doi:10.5455/sajem.040104


Turabian Style

Mansoor, Salman, Shoab Saadat, Sarah Noaman, Hamza Hassan Khan, and Salman Assad.. 2021. Application of machine learning in predicting Attention Deficit Hyperactive Disorder (ADHD) in school going children of Pakistan. South Asian Journal of Emergency Medicine, 4 (1), 18-21. doi:10.5455/sajem.040104


Chicago Style

Mansoor, Salman, Shoab Saadat, Sarah Noaman, Hamza Hassan Khan, and Salman Assad.. "Application of machine learning in predicting Attention Deficit Hyperactive Disorder (ADHD) in school going children of Pakistan." South Asian Journal of Emergency Medicine 4 (2021), 18-21. doi:10.5455/sajem.040104


MLA (The Modern Language Association) Style

Mansoor, Salman, Shoab Saadat, Sarah Noaman, Hamza Hassan Khan, and Salman Assad.. "Application of machine learning in predicting Attention Deficit Hyperactive Disorder (ADHD) in school going children of Pakistan." South Asian Journal of Emergency Medicine 4.1 (2021), 18-21. Print. doi:10.5455/sajem.040104


APA (American Psychological Association) Style

Mansoor, S., Saadat, . S., Noaman, . S., Khan, . H. H. & Assad., . S. (2021) Application of machine learning in predicting Attention Deficit Hyperactive Disorder (ADHD) in school going children of Pakistan. South Asian Journal of Emergency Medicine, 4 (1), 18-21. doi:10.5455/sajem.040104


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