Opinion Mining-Driven Classification Model for Early Autism Spectrum Disorders Identification Based on Standardized Assessments

The efforts to achieve early detection of autism spectrum disorders (ASD) are becoming increasingly important due to the high prevalence that continues to persist globally. The World Health Organization (WHO) and other official institutions agree that in marginalized regions, it is urgently necessary to develop effective alternatives and methods to improve the quality of life of children and their families. This study presents an integrated model for the early detection of ASD, based on the analysis of parental observations and supported by validated diagnostic tools. The proposed approach consists of four sequential modules, aiming to improve early detection through techniques such as natural language processing (NLP) and machine learning (ML) metrics. Records from two Latin American countries were standardized, thereby consolidating a single database comprising 153 records of children aged 2 to 6 years. The Parent Interview Instrument (PII) was administered by specialists to caregivers and subsequently compared with standardized tests. Encouraging results were obtained from the support vector machine (SVM) classification algorithm, yielding an accuracy range of 89.88%–91.34%, a maximum precision of 90.02%, a recall of 89.02%, and a maximum F-measure of 91.12%. The results of the case study allow us to identify disorders related to autism, such as the repetition of behaviors, difficulties in social interaction, and issues with verbal expression. This contribution aligns with the United Nations Sustainable Development Goal 3, which promotes health and well-being.