2026-05-23T07:33:25Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/73402023-03-21T10:52:15Zcom_10259_5841com_10259_5086com_10259_2604com_10259_4219col_10259_5842col_10259_4220

00925njm 22002777a 4500 dc Sáiz Manzanares, María Consuelo author Marticorena Sánchez, Raúl author Arnaiz González, Álvar author 2022-05 Technological advances together with machine learning techniques give health science disciplines tools that can improve the accuracy of evaluation and diagnosis. The objectives of this study were: (1) to design a web application based on cloud technology (eEarlyCare-T) for creating personalized therapeutic intervention programs for children aged 0–6 years old; (2) to carry out a pilot study to test the usability of the eEarlyCare-T application in therapeutic intervention programs. We performed a pilot study with 23 children aged between 3 and 6 years old who presented a variety of developmental problems. In the data analysis, we used machine learning techniques of supervised learning (prediction) and unsupervised learning (clustering). Three clusters were found in terms of functional development in the 11 areas of development. Based on these groupings, various personalized therapeutic intervention plans were designed. The variable with most predictive value for functional development was the users’ developmental age (predicted 75% of the development in the various areas). The use of web applications together with machine learning techniques facilitates the analysis of functional development in young children and the proposal of personalized intervention programs. http://hdl.handle.net/10259/7340 10.3390/ijerph19116558 1660-4601 Early care Web application Machine learning techniques Precision therapeutic program Personalized intervention Disabilities Improvements for Therapeutic Intervention from the Use of Web Applications and Machine Learning Techniques in Different Affectations in Children Aged 0–6 Years