Chronic Kidney Disease (CKD) is a severe kidney damage that is difficult to diagnose at the early stages due to the absence of clear symptoms. Late diagnosis of CKD is a common problem in low-income countries and is often associated with lower chances of survival. This study was designed to develop a user-friendly web-based graphical user interface (GUI) software for the prediction of CKD using artificial neural networks (ANNs). The model was developed using Python programming language and trained with 1200 instances of CKD datasets obtained from the University of California Irvine (UCI) machine learning repository. This dataset was split into 80% for training and 20% for testing achieved through an iterative process. A GUI software was developed based on the model using Django, an open-source python web development framework. The model achieved an accuracy of 95.83%, a precision of 100%, a specificity of 100%, and a sensitivity of 89.80%. The GUI software was effectively used to predict CKD and could be of immense benefit as a point of care application for early CKD prediction

A text-derived neural network for diagnosing Schizophrenia is illustrated in this paper. Schizophrenia is a continuous mental condition that affects the job performance, social relationship, and livelihood of individuals. Using DSM-V criterion for schizophrenia diagnosis, we collected data from medical records of 1205 patients in psychiatric hospitals (57% Schizophrenia and 43% Related Illnesses) and developed a neural network model. In order for the developed model to categorize the test data into classes, significant features from the acquired dataset were fed into it to identify indicators in the training data. The model diagnosed schizophrenia with 90% accuracy, 92% specificity, 84% precision and Area under the Receiver Operating Characteristic (ROC) curve of 0.97. These results are promising for schizophrenia diagnosis in the near future. The text-derived ANN developed is more accurate and faster computationally and can be used to generalize in the case of new data when compared to image-based classification.