Investigating Supervised Models for Identification and Categorization of Network of Correlated Diseases

Molecular biology has exposed several issues and mechanisms of human diseases. To prevent the disease and to further treatment following by diagnosis, this molecular theory is critical. Several investigations indicated that diseases are related to genes. Some kind of correlation exist between diseases and genes. If this correlation is exploited, then it enables to discover the relationships of genes with various diseases. By utilizing the aforementioned aspect, this research study attempts to establish a disease association network. This paper has investigated and evaluated the applicability of various supervised algorithms to generate a disease association network.