Medical Data Mining (MDM) is one of the most critical aspects of automated disease diagnosis and disease prediction. MDM involves developing data mining algorithms and techniques to analyze medical data. In recent years, liver disorders have excessively increased and liver diseases are becoming one of the most fatal diseases in several countries. In this study, two real liver patient datasets were investigated for building classification models in order to predict liver diagnosis. Eleven data mining classification algorithms were applied to the datasets and the performance of all classifiers are compared against each other in terms of accuracy, precision, and recall. Several investigations have also been carried out to improve performance of the classification models. Finally, the results shown promising methodology in diagnosing liver disease during the earlier stages.
Public accountability forums can be useful mechanisms for ensuring that governments are responsive to the perspectives, preferences, and needs of citizens. This study explores how ICTs are affecting public accountability forums and how citizens interact with elected and appointed government officials. A case study of a municipal government in Ontario, Canada, describes how a virtual public accountability forum emerged and functioned based on social media data and interviews with public officials. The case demonstrates that social media can facilitate new channels of interaction between citizens and public officials. Key features of these virtual forums include anonymous participation, variable duration, and dynamic audience size. The case also demonstrates that the demand for information provision within accountability relationships is not just a singular type of ex‐post exchange. There is a new channel of information exchange that is continuous and malleable because it can evolve and change instantaneously over ICTs.
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