Role of Computational Biology in the Diagnosis of Neurodegenerative Disorders.

Abstract

Neurodegenerative disorders present a significant challenge in modern healthcare due to their complex and diverse manifestations. The beginning of computational intelligence has revolutionized the diagnostic landscape, offering promising ways for early and accurate detection of these debilitating conditions. Machine learning algorithms, a subset of computational intelligence, have emerged as powerful tools for analysing extensive datasets comprising genetic, imaging, and clinical information. Furthermore, computational intelligence facilitates the integration of multi-modal data, integrating information from genetic profiles, brain imaging (MRI, PET scans), and clinical assessments. This consolidative approach enables a comprehensive understanding of disease progression and aids in the development of predictive models for early medical assessment and prediction of the outcome. Moreover, the utilization of computational intelligence in neuroimaging analysis has shown remarkable potential. Advanced image processing techniques coupled with machine learning algorithms enable the detection of anatomical and useful abnormalities in the brain, often serving as precursors to neurodegenerative disorders. This chapter explores the vital involvement of computational intelligence within enhancing diagnosis of neurodegenerative disorders, like Parkinson, Alzheimer, etc. In conclusion, computational intelligence offers a transformative framework for advancing the diagnosis of neurodegenerative disorders. Embracing and refining these computational tools will undoubtedly overlay the way for more effective interventions and personalized medicine in the fight against these challenging conditions.