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.
