Titlebook: Advances in Artificial Intelligence; 20th Conference of t Amparo Alonso-Betanzos,Bertha Guijarro-Berdi?as,Al Conference proceedings 2024 Th

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,Predicting Parkinson’s Disease Progression: Analyzing Prodromal Stages Through Machine Learning, data, and Logistic Regression (balanced) when adding thicknesses and volumes of MRI data. The metrics were improve in the second case (AUC ROC of 0.84). Significant predictors include olfactory dysfunction, motor symptoms, psychomotor speed, and third ventricle dilation.

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,An Architecture Towards Building a?Reliable Suicide Information Chatbot, or friends of people who have suicidal ideation can be a valuable resource. This information can be provided by means of chatbot tools; however, the reliability and topicality of the chatbot’s answers should be ensured. In this work, we present an architecture to build a chatbot with the aim of pro

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,O-Hydra: A Hybrid Convolutional and?Dictionary-Based Approach to?Time Series Ordinal Classificationus real-world problems and the possibility to obtain more consistent prediction than nominal Time Series Classification (TSC). Specifically, TSOC involves time series data along with an ordinal categorical output. That is, there is a natural order relationship among the labels associated with the ti

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,Predicting Parkinson’s Disease Progression: Analyzing Prodromal Stages Through Machine Learning,s to discriminate between prodromals that phenoconverted to PD in 7 years to those that did not. Through feature selection, the system identified key first visit predictors of PD phenoconversion, encompassing demographic, clinical, and structural magnetic resonance imaging (MRI) data. Employing seve