Advance Genome Disorder Prediction Model Using Deep Learning Algorithms

A comparative analysis of deep learning techniques for the prediction of genetic disease - cystic fibrosis. Cystic fibrosis (CF) is a complex genetic disorder that needs early and accurate diagnosis for effective treatment. To achieve this, we introduce a powerful genome disorder prediction model that uses advanced deep learning techniques. Our model is built on two key algorithms: Residual Neural Network (ResNet) and Extreme Learning Machine (ELM). We collected a comprehensive dataset that includes genetic data, clinical records, and information about the environment. Our goal was to improve the accuracy of CF prediction. After extensive testing and cross-validation, our research shows that our model performs better than traditional methods. This study is a significant step toward improving the diagnosis and personalized management of cystic fibrosis