E-Intelligence Form Design and Data Preprocessing in Health Care
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Clinical data systems continue to grow as a result of the proliferation of features that are collected and stored. Demands for accurate and well-organized clinical data have intensified due to the increased focus on cost-effectiveness, and continuous quality improvement for better clinical diagnosis and prognosis. Clinical organizations have opportunities to use the information they collect and their oversight role to enhance health safety. Due to the continuous growth in the number of parameters that are accumulated in large databases, the capability of interactively mining patient clinical information is an increasingly urgent need to the clinical domain for providing accurate and efficient health care. Simple database queries fail to address this concern for several problems like the lack of the use of knowledge contained in these extremely complex databases. Data mining addresses this problem by analyzing the databases and making decisions based on the hidden patterns. The collection of data from multiple locations in clinical organizations leads to the loss of data in data warehouses. Data preprocessing is the part of knowledge discovery where the data is cleaned and transformed to perform accurate and efficient data mining results. Missing values in the databases result in the loss of useful data. Handling missing values and reducing noise in the data is necessary to acquire better quality mining results. This thesis explores the idea of either rejecting inappropriate values during the data entry level or suggesting various methods of handling missing values in the databases. E-Intelligence form is designed to perform the data preprocessing tasks at different levels of the knowledge discovery process. Here the minimum data set of mental health and the breast cancer data set are used as case studies. Once the missing values are handled, decision trees are used as the data mining tool to perform the classification of the diagnosis of the databases for analyzing the results. Due to the ever increasing mobile devices and internet in health care, the analysis here also addresses issues relevant hand-held computers and communicational devices or web based applications for quick and better access.