I-Nong Lee | Search PubMed

PubMed2002

Appropriate medical data categorization for data mining classification techniques.

Some data mining (DM) methods, or software tools, require normalized data, others rely on categorized data, and some can accommodate multiple data scales. Each DM technique has a specific background theory; therefore, different results are expected when applying multiple methods. The purpose of this study is to find the data format appropriate for each DM classification technique for wider applications, and efficiently to obtain trustworthy results. Considering the nature of medical data, categorical variables are sometimes useful for making decisions and can make it easier to extrapolate knowledge. In this study, three mathematical data categorization methods (Fusinter, minimum description length principle [MDLPC] and Chi-merge) were applied to accommodate five data mining classification techniques (statistics discriminant analysis, supervised classification with Neural Networks, Decision trees, Genetic supervised clustering and Bayesian classification [probability neural networks; PNN]) using a heart disease database with four types of data (continuous data, binary data, nominal data, and ordinal data). Compared with original or normalized data, data categorized by the MDLPC categorization method was found to perform better in most of the DM classification techniques used in this study. Categorical data is good for most DM classification techniques (e.g. classification of disease and non-disease groups) and is relatively easy to use for extracting medical knowledge.

Bayes Theorem↗

PubMed2002

Important variable selection techniques with multiple solutions for medical information applications.

The purpose of this study is to utilize the strategy of multiple solutions with easily operated tools to make compatible and affordable trustworthy results. This strategy can satisfy general users in extracting specific knowledge (e.g. diagnosis, treatment, health education, hospital administration, etc.). Wide application is one of the key promoters of a successful medical information system. Risk factors of heart disease can be identified by important variable selection techniques. Four techniques, Chi-square test, correlation analysis, stepwise discriminant analysis, and decision trees, were used in this study. One must compile the results of the different methods to deal with specific research questions in a logical way. It is expected that this approach may reduce the uncertainties obtained from a single method. When applying the important index derived from the four different method results to several applications, all results have been confirmed by current medical knowledge or otherwise provide meaningful information. The prime purpose of this study is not disease diagnosis, but to enable the general health practitioner (medical doctor, hospital administrator, etc.) to formulate their hypothesis.

Case-Control Studies↗