Early Risk Detection of Genetic Disorders in Newborns using Cuckoo Search and Recurrent Neural Networks

Early detection of genetic defects in newborns is required for prompt medical intervention leading to improved health outcomes. A new model framework is introduced in this paper that aims to increase the detection accuracy of potential early risks with Cuckoo type Search Optimization (CSO) and Recurrent based Neural Networks (RNNs) linking.The Cuckoo type Search Algorithm is used for optimizing the selection of features and keeping the key genes with less computational complexity. The RNN on its part, because of its ability to remember the crucial parts of genetic data in a certain period, makes it possible for the model to execute the most precise classification of the severe cases. The developed method is a combination of CSO and RNN-learning where the process of CSO improves the set of extracted features thus making the Rnn function faster and better. The testing of a new system has concluded that, in terms of accuracy, sensitivity, as well as early detection rate, higher quality has reached than that of previous methods. The tight thereof of CSO and RNN development not only makes predictions more reliable but also the system becomes scalable to newborn genetic screening that gives better opportunities for healthcare planning.