ML based Pre-Diagnosis Framework for early detection of Neurodevelopmental Disorders

Neurodevelopmental disorders cover a wide range of behavioral and cognitive symptoms mapped to various conditions. Early diagnosis and planned interventions are crucial for improving outcomes and the quality of life for people suffering from such disorders. This research work talks about a detailed framework with the objective of assisting in the early pre-diagnostic phase of neurodevelopmental disorders. The motivation for the development of a ML-based framework in pre-diagnosis of neurodevelopmental disorders is the effective and zero-cost getting diagnosed. This framework will integrate knowledge from diverse domains to systematize and make recognition of early signs and symptoms associated with neurodevelopmental disorders much more efficient. Additionally, the framework emphasizes two very critical parameters: accessibility and cost-effectiveness. The developed pre-diagnostic mechanism can be implemented in a diversified variety of scenarios, especially with limited resources, towards zero cost. The implementation of advanced machine learning techniques can assist in the analysis of the response to a customized questionnaire in identifying patterns that may suggest a risk of neurodevelopmental disorders. This proactive approach not only supports and facilitates early detection and intervention but also seeks to add towards the overall well-being of patients with these disorders.