Precision Prediction of Schizophrenia: Harnessing Genetic and Environmental Factors

The World Health Organization (WHO) estimates that Schizophrenia affects more than 24 million people around the globe or it could be said that one in 300 people are suffering from this mental condition. Schizophrenia is a chronic disorder that alters the thinking and behavioral aspects of a person’s life. Studies have failed to detect the actual reasons for Schizophrenia. Due to its complex etiology and multivariate symptoms, the disorder becomes difficult to be diagnosed on time. Existing studies suggest that genetic factors have an important role in spreading Schizophrenia through heritability. Environmental factors are yet to be examined as adequate data for social and ambient factors is not available for existing cases. This study aims to integrate genetic and natural influences to provide a self-learning model that could predict the existance of the mental disorder based on possible factors. The proposed model focuses on implementing supervised and unsupervised learning for accurate predictions based on previous data. The model is expected to provide better projections and analysis for schizophrenic patients and valuable insights to the complex nature of Schizophrenia. Future aspects of this research include exploring the root causals and improving time constraints for predictions. Moreover, suggestions to get early diagnosis and cure can contribute to the community for global mental well being.