May Big Data Analysis Be Used to Diagnose Early Autism?
In this paper, a technique for early autism identification is presented. Both a multi-layered perceptron (MLP) neural network and a support vector machine (SVM) have been used for classification. Detection of early autism is important, since the prognosis to treat autism is then much better. The pat...
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2023
|
| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/85802 |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | In this paper, a technique for early autism identification is presented. Both a multi-layered perceptron (MLP) neural network and a support vector machine (SVM) have been used for classification. Detection of early autism is important, since the prognosis to treat autism is then much better. The patterns of both methods to use have been extracted from high-performance liquid chromatography data in urine. The training samples consist of two types, one from normal children and one from children with autism. The classification rate has been estimated for both algorithms to about 80% or better. The algorithm that gave the best result was SVM. The program that we used to do the analysis we have developed in Java. A lot of work remains to improve the results and increase the recognition rate of the data. The parameter values used in both networks and also the configuration of the networks are not yet optimal. This could be solved by using a particle swarm optimization (PSO) method. We have not yet been using a deep learning network, for instance, a TensorFlow network to raise the classification rate of the different algorithms. We have not yet made a classification between different types of autism of the autism spectrum. All this belongs to future work. |
|---|---|
| ISBN: | 978-1-83768-342-0 |
| DOI: | 10.5772/intechopen.109537 |