Interpretable Data Driven Classifiers: A proposal for Autism Diagnosis of Children Using Ensemble Learning

Autism Spectrum Disorder (ASD) is a significant healthcare concern due to the large number of cases detected annually, and the massive resources required to support individuals on the spectrum and their families. Data mining and artificial intelligence (AI) techniques have shown promising results in research on healthcare applications, including ASD diagnosis, by providing accurate diagnosis. However, most data models developed by these intelligent techniques, a) do not provide details behind the diagnostic decision to the stakeholders such as clinicians, patients, and caregivers, and b) are criticised for being biased to a single data model rather a group of models. A model that can interpret results involved in the diagnostic process is advantageous offering digital knowledge to healthcare professionals besides adhering to the General Data Protection Regulation (GDPR) terms primarily 'results derived by automated decision-making methods' like AI techniques. More essentially, when the prediction is performed by a group of models this can reduce the decision bias of the diagnosis. This article fills these gaps by proposing a framework based on ensemble learning where a rule-based classifier develops interpretable data models for ASD diagnosis.