Deep Learning for Autism Spectrum Disorder: A Multifaceted Analysis of Genetic, Environmental, and Neurological Influences
Autism Spectrum Disorder (ASD) is a neurological disorder that is affected by genetic, environmental, and neurological determinants. Early prediction of ASD can have a substantial positive impact on the intervention and support of affected people. The proposed paper suggests a deep learning framework, combining genetic, environmental, and neurological data to predict ASD. Although the model proves to be very accurate in its prediction, a significant area of implementation of such predictive systems in practical healthcare implementation is the security and privacy of sensitive data in real-life healthcare applications. This paper also focuses on the security concerns and measures of wireless mobile networks and ubiquitous computing because of the growing popularity of mobile and wireless networks in healthcare. The ability to transmit genetic and neurological information across systems and platforms safely, without losing privacy and trust, is essential to the practical implementation of the model. The results denote the necessity to embrace effective security systems in the context of mobile-based ASD prediction instruments that guarantee the quality and reliability of medical solutions.