Abstract
Abstract: Brain disorders represent a significant and growing global health challenge, encompassing a spectrum of conditions ranging from neurodegenerative diseases, like Alzheimer's and Parkinson's, to various neuropsychiatric disorders. Timely and accurate detection of brain disorders is crucial for enabling prompt interventions, tailoring appropriate treatments, and improving the overall quality of life for affected individuals. This research paper explores an innovative and interdisciplinary approach to enhance the process of brain disorder detection through the integration of brainstorming techniques and advanced machine learning algorithms. The research delves into the adaptability and effectiveness of brainstorming techniques for brain disorder detection, leveraging diverse data sources such as clinical records, genetic information, and patient histories. The study presents various brainstorming techniques that are utilised to analyse, identify and arrive at the best possible methodology that can be used for the detection of brain disorders.
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