A Novel Algorithm to Diagnosis Type II Diabetes Mellitus Based on Association Rule Mining Using MPSO-LSSVM with Outlier Detection Method
作者： T. Karthikeyan , K. Vembandasamy
DOI: 10.17485/IJST/2015/V8IS8/53631
关键词:
摘要: Background/Objectives: The execution of Frequent Pattern Growth algorithm on medical data is difficult. Association rule based classification an interesting area focused that can be utilized for early diagnosis. Methods/Statistical analysis: Discretization phase necessary to transform numerical characteristics. results are given Complete Patten Growth++ the purpose induction. Accordingly, using Modified Particle Swarm Optimization together with Least Squares Support Vector Machine scheme (MPSO-LSSVM) rules produced outlier detection method. Pima Indians Diabetes Data Set taken as input. time, number generation and percentage analyzed. Results: CFP-growth utilizes finding frequent patterns where constructing Minimum Item (MIS)-tree, CFP-array producing from MIS-tree. From set item sets found, create all association have a confidence exceeding minimum confidence. Enhanced method used determining degree detection. mined MPSO-LSSVM mining algorithm. first time in this work For reason eradicating effect unavoidable outliers investigation sample scheme’s performance, new integration proposed time. experimental observations reveal framework provides better accuracy 95% when evaluated against existing techniques. Conclusion/Application: CFP-Growth++ pruning Type-2 DM dataset. This suitable type-2 diabetes mellitus disease.
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