An Intelligent Type-II Diabetes Mellitus Diagnosis Approach using Improved FP-growth with Hybrid Classifier Based Arm

摘要

Diabetes mellitus has turned out to be a common chronic disease that affects between 2 and 4% of the total population. Recently, most of the system uses association rule mining for diagnosing type-II diabetes mellitus. The most vital concern of association rules is that rules are derived from the complete data set with no validation on samples. Previously, Association rule based Modified Particle Swarm Optimization and Least Squares Support Vector Machine classification is introduced with the capability to lessen the number of rules, looks for association rules on a training set and at last validates them on an independent test set. On the other hand, it only employs categorical data. In case of Type-II Diabetes Mellitus medical diagnosis, the exploitation of continuous data might be essential. With the aim of solving this complication, Improved Frequent Pattern Growth (IFP-Growth) with Hybrid Enhanced Artificial Bee Colony-Advanced Kernel Support Vector Machine (HEABC-AKSVM-IFP Growth) classification based Association Rule Mining (ARM) system is proposed in this study to create rules. This study introduces improved FP-growth to effectively derive frequent patterns including from a vague database in which items possibly will come into view in medical database. Then, HEABC-AKSVM-IFP Growth classifier is employed to create the association rules from the frequent item sets, also keeping away from the rule redundancy and inconsistencies at the time of mining process. Then, results are simulated and evaluated against few classification techniques in terms of classification accuracy, number of derived rules and processing time.