Using Machine Learning Techniques in eEarlyCare Precision Diagnosis and Intervention in 0–6 years Old

Abstract

The use of Machine Learning techniques and technological resources that facilitate diagnosis and intervention at early ages (0–6 years) will facilitate the development of both processes from the point of view of accuracy. This paper analyses the most useful Machine Learning techniques to be applied to diagnosis and therapeutic intervention in the field of early care. It also describes the development of a web application, eEarlyCare, which includes the recording and interpretation of the results through Learning Analytics techniques of the observation of different early development problems. In addition, an evaluation of the usability of this web application is carried out as part of a training project aimed at updating the technological and data analysis strategies of early intervention professionals. The results support the use of this type of computer applications in which learning analytics and visualisation of results techniques are included. The proposals for improvement focus on the use of technological resources similar to intelligent voice assistants that regulate the work of the therapy professional. Further studies will address these proposals for improvement.