# Comparison of Classification Algorithms of the Autism Spectrum Disorder Diagnosis
> Lawi A.

URL kanonis: https://discover.unhas.ac.id/publications/comparison-of-classification-algorithms-of-the-autism-spectrum-disorder-diagnosi
Jurnal / Konferensi: Proceedings 2nd East Indonesia Conference on Computer and Information Technology Internet of Things for Industry Eiconcit 2018
Tahun terbit: 2018
DOI: https://doi.org/10.1109/EIConCIT.2018.8878593
Citations: 11

## Authors
- Lawi A.

## Abstract
ASD sufferers face difficulties in early development compared to normal humans. Various tools, clinical, and non-clinical approaches have been implemented but take a long time to produce a complete diagnosis. the solution by adopting machine learning. This study proposes the application of cross-validation techniques in the Decision Tree method, Linear Discriminant Analysis, Logistic Regression, SVM, and KNN and determines the best k value in each classification method because the shift of datasets when using cross-validation techniques in the classification method is one factor that can cause the estimate to be inaccurate. The results show that the decision tree provides an accuracy of 100% in each of the k values that have been determined previously. 96.9% on Linear Discriminant Analysis with $k=7, k=9$, and $k =10$. 99.7% in Logistic Regression with values of $k=2$ and $k= 3$. 99.9% in Support Vector Machine with values of $k=9$ and $k =1\theta$ and 94.2% for K-Nearest Neighbors with a value of $k=8$.

## Keywords
- Linear discriminant analysis
- Decision tree
- Support vector machine
- Artificial intelligence
- Logistic regression
- Logistic model tree
- Pattern recognition (psychology)
- Discriminant
- Computer science
- Autism spectrum disorder
- Statistical classification
- Value (mathematics)
- Machine learning
- Algorithm
- Mathematics
- Autism
- Psychology
- Developmental psychology

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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