E-ISSN 2636-834X
 

Original Research 


Can We Predict Psychiatric Disorders at the Adolescence Period in Toddlers? A Machine Learning Approach

Mirac Baris Usta, Koray Karabekiroglu, Gokce Nur Say, Yusuf Yasin Gumus, Muazzez Aydin, Berkan Sahin, Abdullah Bozkurt, Tolga Karaosman, Armagan Aral, Cansu Cobanoglu, Aysegul Duman Kurt, Neriman Kesim, Irem Sahin.

Abstract
Objectives: Recent studies show emotional and behavioral problems in toddlerhood affecting later stages of development. However, the predictive factors for psychiatric disorders were not studied with machine learning methods. We aimed to examine the predictors of outcome with machine learning methods, which are novel computational methods including statistical estimation, information theories, and mathematical learning automatically discovering useful patterns in large amounts of data.

Method: The study group comprised 116 children (mean age: 27.4±4.4 months) who are evaluated between 2006-2007 years in a clinical setting. Emotional and behavioral problems were assessed by the Brief Infant-Toddler Social Emotional Assessment and Child Behavior Checklist/2-3.Child psychiatry residents made follow-up evaluations with telephone calls in 2018. We tested the performance of for machine learning algorithms (Decision tree, Support Vector Machine, Random Forest, Naive Bayes, Logistic Regression) on our data, including the 254 items in the baseline forms to predict psychiatric disorders in adolescence period.

Results: 26.7% (n: 31) of the cases had diagnosed with a psychiatric disorder in adolescence period. In machine learning methods Random Forest outperforms other models, which had reached an accuracy of 85.2%, AUC: 0.79. Our model showed BITSEA item 20, 13, and CBCL total external problems scores filled by mother at the age of 12-36 months are the most potent factors for a psychiatric disorder in adolescence.

Conclusion: We found very early behavioral and emotional problems with sociodemographic data predicted outcome significantly accurately. In the future, the machine learning models may reveal several others are more important in terms of prognostic information and also planning treatment of toddlers.

Key words: toddlerhood, predictive, machine learning, adolescent, psychiatry disorder


 
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How to Cite this Article
Pubmed Style

Usta MB, Karabekiroglu K, Say GN, Gumus YY, Aydin M, Sahin B, Bozkurt A, Karaosman T, Aral A, Cobanoglu C, Kurt AD, NK, Sahin I. Can We Predict Psychiatric Disorders at the Adolescence Period in Toddlers? A Machine Learning Approach. PBS. 2020; 10(1): 7-12. doi:10.5455/PBS.20190806125540


Web Style

Usta MB, Karabekiroglu K, Say GN, Gumus YY, Aydin M, Sahin B, Bozkurt A, Karaosman T, Aral A, Cobanoglu C, Kurt AD, NK, Sahin I. Can We Predict Psychiatric Disorders at the Adolescence Period in Toddlers? A Machine Learning Approach. https://www.pbsciences.org/?mno=60359 [Access: December 04, 2023]. doi:10.5455/PBS.20190806125540


AMA (American Medical Association) Style

Usta MB, Karabekiroglu K, Say GN, Gumus YY, Aydin M, Sahin B, Bozkurt A, Karaosman T, Aral A, Cobanoglu C, Kurt AD, NK, Sahin I. Can We Predict Psychiatric Disorders at the Adolescence Period in Toddlers? A Machine Learning Approach. PBS. 2020; 10(1): 7-12. doi:10.5455/PBS.20190806125540



Vancouver/ICMJE Style

Usta MB, Karabekiroglu K, Say GN, Gumus YY, Aydin M, Sahin B, Bozkurt A, Karaosman T, Aral A, Cobanoglu C, Kurt AD, NK, Sahin I. Can We Predict Psychiatric Disorders at the Adolescence Period in Toddlers? A Machine Learning Approach. PBS. (2020), [cited December 04, 2023]; 10(1): 7-12. doi:10.5455/PBS.20190806125540



Harvard Style

Usta, M. B., Karabekiroglu, . K., Say, . G. N., Gumus, . Y. Y., Aydin, . M., Sahin, . B., Bozkurt, . A., Karaosman, . T., Aral, . A., Cobanoglu, . C., Kurt, . A. D., , . N. K. & Sahin, . I. (2020) Can We Predict Psychiatric Disorders at the Adolescence Period in Toddlers? A Machine Learning Approach. PBS, 10 (1), 7-12. doi:10.5455/PBS.20190806125540



Turabian Style

Usta, Mirac Baris, Koray Karabekiroglu, Gokce Nur Say, Yusuf Yasin Gumus, Muazzez Aydin, Berkan Sahin, Abdullah Bozkurt, Tolga Karaosman, Armagan Aral, Cansu Cobanoglu, Aysegul Duman Kurt, Neriman Kesim, and Irem Sahin. 2020. Can We Predict Psychiatric Disorders at the Adolescence Period in Toddlers? A Machine Learning Approach. Psychiatry and Behavioral Sciences, 10 (1), 7-12. doi:10.5455/PBS.20190806125540



Chicago Style

Usta, Mirac Baris, Koray Karabekiroglu, Gokce Nur Say, Yusuf Yasin Gumus, Muazzez Aydin, Berkan Sahin, Abdullah Bozkurt, Tolga Karaosman, Armagan Aral, Cansu Cobanoglu, Aysegul Duman Kurt, Neriman Kesim, and Irem Sahin. "Can We Predict Psychiatric Disorders at the Adolescence Period in Toddlers? A Machine Learning Approach." Psychiatry and Behavioral Sciences 10 (2020), 7-12. doi:10.5455/PBS.20190806125540



MLA (The Modern Language Association) Style

Usta, Mirac Baris, Koray Karabekiroglu, Gokce Nur Say, Yusuf Yasin Gumus, Muazzez Aydin, Berkan Sahin, Abdullah Bozkurt, Tolga Karaosman, Armagan Aral, Cansu Cobanoglu, Aysegul Duman Kurt, Neriman Kesim, and Irem Sahin. "Can We Predict Psychiatric Disorders at the Adolescence Period in Toddlers? A Machine Learning Approach." Psychiatry and Behavioral Sciences 10.1 (2020), 7-12. Print. doi:10.5455/PBS.20190806125540



APA (American Psychological Association) Style

Usta, M. B., Karabekiroglu, . K., Say, . G. N., Gumus, . Y. Y., Aydin, . M., Sahin, . B., Bozkurt, . A., Karaosman, . T., Aral, . A., Cobanoglu, . C., Kurt, . A. D., , . N. K. & Sahin, . I. (2020) Can We Predict Psychiatric Disorders at the Adolescence Period in Toddlers? A Machine Learning Approach. Psychiatry and Behavioral Sciences, 10 (1), 7-12. doi:10.5455/PBS.20190806125540