Navegando por Palavras-chave "Feature Extraction"

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    Reconhecimento automático de padrões em dislexia: uma abordagem baseada em funções visuais da leitura e aprendizado de máquina
    (Universidade Federal de São Paulo (UNIFESP), 2019-12-16) Silva Junior, Antonio Carlos Da [UNIFESP]; Mancini, Felipe [UNIFESP]; Schor, Paulo [UNIFESP]; Gonçalves, Emanuela Cristina Ramos [UNIFESP]; http://lattes.cnpq.br/3542867700396961; http://lattes.cnpq.br/8425496220946395; http://lattes.cnpq.br/4433119488921195; http://lattes.cnpq.br/1464083566861583; Universidade Federal de São Paulo (UNIFESP)
    INTRODUCTION: Developmental dyslexia is a neurological disorder that affects reading ability, that when left untreated can lead to learning problems and negatively affecting vocabulary increase. The diagnosis of dyslexia is complex and made by exclusion. Some studies evaluated eye movement data in conjunction with machine learning (ML) techniques to classify dyslexia. Another study raises the hypothesis of visual reading function patterns (VRF) for dyslexic differentiation. The study of VRF in combination of ML techniques has not been explored. GENERAL OBJECTIVE: To apply ML techniques to explore and assist the diagnosis of dyslexics from VRF. SPECIFIC OBJECTIVES: To explore dyslexic and non-dyslexic VRF data with feature extraction and to classify dyslexic and non-dyslexic using ML. MATERIAL AND METHODS: This dissertation has two steps: a quantitative and exploratory and a quantitative and correlational. The first step explored two dyslexic VRF datasets, one of 1-line (1L) text readings and the other of 3-line (3L) text readings. The self-organizing map algorithm was applied to each base to separate them into clusters that were then sent to a decision tree to extract the rules characterize each of the groups. The second step used data from 3L readings. The outliers was selected by a specialist. With the remaining data, the SMOTE algorithm was applied. Then a feature selection technique was applied having the best area under the ROC curve (AUC) as target for each of the five selected algorithms. They were compared by AUC and accuracy. All were also compared by their calibration curve. RESULTS: In the first step, the 1L base evaluation resulted in a clustering of 1 cluster of controls and 3 of dyslexics. Only dyslexics obtained Maximum reading speed MRS <140.72 ppm, while in the 3L evaluation, 3 dyslexic clusters and 1 control were obtained. In this only dyslexics had reading speed at critical read size (RSCPS) of less than 112.71 ppm. In the second step, synthetic data were generated for each group to have 100 records. In feature selection, the reading acuity (RA) was selected in 4 of the 5 algorithms. Logistic regression obtained the best AUC (0.999) and accuracy (99%) and obtained the best calibration curve. CONCLUSION: In the first step, the fact that MRS was so determinant in the separation of the 1L clusters and the RSCPS in the first one. It may indicate that the crownding effect had some impact on the 3L test. The fact that RA has been selected in 4 of the 5 feature selections may be an important variable for the diagnosis and study of dyslexia. The logistic regression algorithm obtained the best results and was indicated for VRF-based dyslexic classification.