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• W295492007 abstract "Decision rules are commonly used tool for classification and knowledge discovery in data. The aim of this paper is to provide decision rule-based framework for analysis of survival data and apply it in mining of data describing patients after bone marrow transplantation. The paper presents a rule induction algorithm which uses sequential covering strategy and rule quality measures. An extended version of the algorithm gives the possibility of taking into account user’s requirements in the form of predefined rules and attributes which should be included in the final rule set. Additionally, in order to summarize the knowledge expressed by rule-based model, we propose the rule filtration algorithm which consists in selection of statistically significant rules describing the most disjoint parts of the entire data set. Selected rules are identified with so-called survival patterns. The survival patterns are rules which conclusions contain Kaplan-Meier estimates of survival function. In this way, the paper combines rule-based data classification and description with survival analysis. The efficiency of our method is illustrated with the analysis of data describing patients after bone marrow transplantation. Frequent task in medical research studies is an analysis of gathered research material in terms of searching for some patterns. In addition to statistical methods, data mining and knowledge discovery are becoming more popular for such studies [6]. In this paper, we apply the decision rule induction algorithm to analyze survival data. Decision rule induction [14],[18],[20],[26],[31],[45],[52] is widely used knowledge discovery method applied for pattern identification in data. We use decision rules for two basic aims. One is the prediction of whether the patient would survive longer than given time. The other is the description of patient groups with different survival characteristics. In particular, we present a way of associating decision rules with Kaplan-Meier analysis [30]. We also improve readability of the rule-based model with the use of rule filtration algorithm which selects the rules describing the most disjoint groups of patients. Rule induction algorithms automatically select attributes and ranges of attributes which occur in their premises. This causes the situations in which induced rules not always contain the most important information for a user. We propose a modification of the rule induction algorithm which allows taking into account user’s requirements. These requirements are defined as the initial set of rules and attributes which should be included in the final rule set. The analysis of results obtained by various rule induction algorithms leads to the statement that description and classification abilities of induced rules depend, among others, on measures applied to" @default.
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• W295492007 date "2013-01-01" @default.
• W295492007 modified "2023-10-03" @default.
• W295492007 title "APPLICATION OF RULE INDUCTION TO DISCOVER SURVIVAL FACTORS OF PATIENTS AFTER BONE MARROW TRANSPLANTATION" @default.
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