Data mining

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There are a number of data mining tools that can be used in nursing or healthcare administration. These tools include RapidMiner; WEKA; KNIME; and Python based Orange.

The technical requirements for RapidMiner include at least 100GB of hard disk space and a 64-bit operating system can work with various types of databases, including MySQL; Microsoft SQL Server; Oracle; PostgreSQL. This tool offers advanced analytics because of the use of template based frameworks, thus limiting the need for coding skills. It provides data visualization and preprocessing that aids to prediction based analytics. The advantage of Rapid Miner as a data mining tool is that it has a wide range of functionalities while the disadvantage is that it might not be a good option for new users.

The technical requirements for WEKA include: Windows, Linux, or OS X operating systems; SQL database; and Java Database Connectivity.   The tasks offered by the tool include data preprocessing, classification, clustering, regression, feature selection, and visualization. The advantages of this tool include free availability; a graphical user interface; and portability. The disadvantages include the fact that it is not suitable for complex level of data mining.

Python based Orange has advanced in popularity because of its powerfulness and ease of use. Its technical requirements include: Windows, Linux, or OS X operating systems; MySQL; and Python Database Connectivity.  It is a preferable tool because of the multilayer architecture interface, a user interphase that is graphic; and has various tools and functions (Yoo, Alafaireet, Marinov, Pena-Hernandez, Gopidi, Chang & Hua, 2012). One of the advantages is that the ease of using this tool for data mining cannot be compared to any of the available alternatives. The graphical user interface makes it very user-friendly. The disadvantage is that it is not suitable for intensified classical Statistics because of the lack of widgets.

The requirements for KNIME include Linux, Windows, or Max OSX operating systems; main memory of at least 1 GB; a 64bit system; and tens of GB space that will be used for data processing. Features of this tool include an intuitive user interface; provision of modular visual platform which enables the exploration of data and cleansing. The advantages include that fact that it was developed using Java platform, which makes it suitable for all operating systems.  People who have zero skills in coding can handle it. The disadvantage comes in the fact that it has limited functionalities are compared to the available data mining alternatives.

Of the data mining tools that have been described herein, RapidMiner is the best alternative to be used in the healthcare industries.  One of the reasons is the ability of the tool to offer advanced analytics without having to complicate the manner in which the users have to interact with the tool. It would also be preferred because of its ability to simplify the process of validation. Data and information play very important roles in healthcare (Milovic & Milovic, 2012). Therefore, the industry could use a data mining tool that is powerful, has multiple functionalities and support and is not complicated. This is a nature of RapidMiner that make it preferable for many healthcare setting because it saves the time that is needed for data mining without compromising the quality of service. The user interface is also good and enables users to have an enjoyable experience and easy time when it is used in the various industries under healthcare.

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  1. Milovic, B., & Milovic, M. (2012). Prediction and decision making in health care using data mining. Kuwait Chapter of the Arabian Journal of Business and Management Review, 1(12), 126.
  2. Yoo, I., Alafaireet, P., Marinov, M., Pena-Hernandez, K., Gopidi, R., Chang, J. F., & Hua, L. (2012). Data mining in healthcare and biomedicine: a survey of the literature. Journal of medical systems, 36(4), 2431-2448.
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