Analysis of Autism Spectrum Disorder in Children with Data Mining Methods
Data mining techniques aim to reveal hidden patterns in data. They are widely used in many fields, such as medicine. Autism spectrum disorder, whose diagnosis and treatment are difficult and lengthy, is a complex neurodevelopmental disorder that is congenital or occurs in the first years of life. Ac...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Istanbul University Press
2021-06-01
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| Series: | Acta Infologica |
| Subjects: | |
| Online Access: | https://cdn.istanbul.edu.tr/file/JTA6CLJ8T5/4C99D0EF27B54DCC9135FF2A2219B25A |
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| Summary: | Data mining techniques aim to reveal hidden patterns in data. They are widely used in many fields, such as medicine. Autism spectrum disorder, whose diagnosis and treatment are difficult and lengthy, is a complex neurodevelopmental disorder that is congenital or occurs in the first years of life. Actual and current autism spectrum disorder data collected from 292 children were used in this study. The data set has 20 input attributes and 1 output attribute. The output attribute expresses whether autism is present or not. In the study, data preprocessing stages, such as completing missing data on the data set, digitizing categorical data, and normalization, were first carried out. Subsequently, the features were classified by artificial neural networks and linguistic strength neuro-fuzzy classifier and clustered with k-means and x-means. The results of each method were evaluated and the performances were compared. |
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| ISSN: | 2602-3563 |