Development of an Expert System for Diagnosing Neurological Disorders in Children Using Artificial Neural Networks
Keywords:
Neurological Disorders, Pediatric Diagnosis, Artificial Neural Networks, Expert System, Healthcare AIAbstract
Neurological disorders in children, including conditions such as cerebral palsy, epilepsy, autism spectrum disorder (ASD), and attention deficit hyperactivity disorder (ADHD), pose significant challenges to early diagnosis and intervention. Early and accurate diagnosis is essential for improving outcomes, yet the process is often complex, time-consuming, and reliant on specialized clinical expertise. This research proposes the development of an expert system powered by Artificial Neural Networks (ANNs) to assist healthcare professionals in diagnosing pediatric neurological disorders. The system aims to analyze a wide range of data inputs, including patient symptoms, medical history, and diagnostic tests, to deliver accurate and timely diagnoses. The study evaluates the system's diagnostic accuracy, efficiency, and usability through testing with real-world clinical data. Preliminary results suggest that the expert system can offer high diagnostic accuracy, significantly reduce diagnostic turnaround times, and improve the decision-making process for clinicians. Furthermore, the system’s potential to be used in resource-limited settings highlights its broader implications for improving healthcare access and reducing disparities in pediatric neurology. The research contributes to the growing field of AI in healthcare by providing an innovative, scalable solution to address the challenges of diagnosing neurological disorders in children, ultimately improving the quality of care and long-term outcomes for affected children.
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