Abstract
Medical data mining has great potential for exploring the hidden pattern in the data sets of the medical domain. A predictive modeling approach of Data Mining has been systematically applied for the prognosis, diagnosis, and planning for treatment of chronic disease. For example, a classification system can assist the physician to predict if the patient is likely to have a certain disease, or by considering the output of the classification model, the physician can make a better decision on the treatment to be applied to the patient. Once the model is evaluated and verified, it may be embedded within clinical information systems. The objective of this chapter is to extensively study the various predictive data mining methods to evaluate their usage in terms of accuracy, computational time, comprehensibility of the results, ease of use of the algorithm, and advantages and disadvantages to relatively naive medical users. The research has shown that there is not a single best prediction tool, but instead, the best performing algorithm will depend on the features of the dataset to be analyzed.
Top2. Data Mining Method And Its Classification
The KDD is responsible to transform low-level data into high-level knowledge for decision making. Data mining being one of the important steps of KDD is the nontrivial process of identifying valid, new, potentially useful, and ultimately understandable patterns in data. The two primary goals of data mining tend to be prediction and description. Prediction involves using some variables or fields in the data set to predict unknown or future values of other variables of interest. Description, on the other hand, focuses on finding patterns describing the data that can be interpreted by humans. Therefore, it is possible to put data-mining activities into one of two categories.
Predictive data mining: Predictive models can be used to forecast explicit values, based on patterns determined from known results. This technique is becoming an essential instrument for researchers and clinical practitioners in medicine. These methods may be applied to the construction of decision models for procedures such as prognosis, diagnosis and treatment planning, which – once evaluated and verified –may be embedded within clinical information systems. Classification and prediction are major predictive data mining task.
In clinical medicine predictive modeling is particularly useful tool for decision making using patient data. The goal is to derive models that can use patient specific information to predict the outcome of interest and to thereby support clinical decision-making. Such models may be used by clinicians and allow a prompt reaction to unfavorable situations.
Descriptive data mining: The goal of descriptive data mining is to gain an understanding of the analyzed system by uncovering patterns and relationships in large data sets. Association rule mining (ARM) is one of the most important and well researched techniques of data mining for descriptive task. The Descriptive Mining techniques include Clustering, Association Rule Discovery and Sequential Pattern Discovery, are used to find human-interpretable patterns that describe the data in the form of clusters, itemsets, association rules and sequential patterns. Descriptive modeling is not directly useful in clinical medicine for example ARM will only association among various symptoms where as clustering methods will simply group the patient based on their disease.