Two-dimensional visualization of classification and regression problems: automatic prediction of behavior from sensory data in autism spectrum disorder

Autores: Heba Basheer Saeed Alateyat
Directores de la Tesis: Manuel Fernández Delgado (dir. tes.) , Eva Cernadas García (dir. tes.)
Lectura: En la Universidade de Santiago de Compostela ( España ) en 2023
Idioma: inglés
Número de páginas: 131
Tribunal Calificador de la Tesis: Arno Formella (presid.) , Pablo García Tahoces (secret.) , Manisha Sanjay Sirsat (voc.)
Enlaces
- Tesis en acceso abierto en: MINERVA
Resumen
- This thesis formulates methods to perform classification and regression by projecting high-dimensional patterns in two dimensions. These methods create a 2D classification or regression map to visualize the data as a political (for classification) or temperature (for regression) map, where each pixel in the map has an associated prediction. The thesis also uses 26 machine learning models for the automatic prediction of behavior in the treatment of autism spectrum disorder using sensory processing information. Behavior and sensory data are extracted from their respective questionnaires. Out of 11 behavior outcomes, the prediction of externalizing problems is very reliable and accurate enough in other 7 outcomes.