Genomic Risk Assessment and Early Intervention for Rare Genetic Disorders

Genetic diseases pose significant challenges due to their complex nature and the need for specialized diagnostic techniques. This research focuses on developing a comprehensive framework for classifying genetic diseases using advanced machine learning algorithms. By applying techniques such as random forest classification, artificial neural networks, and support vector machines, the proposed framework aims to extract complex genetic data and accurately identify various genetic diseases. This approach empowers individuals to become more aware of their genetic health and take proactive steps for prevention and early intervention. The research contributes to the field of healthcare by providing a data-driven solution for genetic disease classification. By integrating new technologies like the chain classifier approach, the framework enhances diagnostic accuracy and addresses the complexities associated with genetic interactions. This work represents a significant step towards improving the understanding and management of genetic diseases, ultimately contributing to global health.