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

T Karthikeyan,B Raghavan,K Vembandasamy

doi:10.19026/rjaset.11.1860

T Karthikeyan, B Raghavan + Show 1 more

Open Access

https://doi.org/10.19026/rjaset.11.1860

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Abstract

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.

Highlights

Diabetes mellitus is a major problem and continues to affect many people and its prevention and efficient treatment are unquestionably fundamental
Following the preprocessing phase discussed in above section, executed Class Association Rules (CAR)-RG based on HEABCAKSVM-IFP Growth to produce the refined rules
The results of proposed HEABC-AKSVM-IFP Growth are compared against the existing ABC-LSSVM-IFP Growth, MPSO-LSSVM-CFP Growth++ and Support Vector Machines (SVMs)-Frequent Pattern (FP) Growth based classification technique based on accuracy rate, runtime and number of rules produced etc

Summary

Introduction

Diabetes mellitus is a major problem and continues to affect many people and its prevention and efficient treatment are unquestionably fundamental. Regardless of current medical advancements, premature diagnosis of disease has developed almost half of the patients of type II diabetes are ignorant of their disease and possibly will take more than ten years as the delay from disease beginning to diagnosis at the same time early diagnosis and treatment of this disease is very important. Classification systems have been extensively employed in medical domain to discover patient’s data and derive a predictive model. They assist physicians to advance their prognosis, diagnosis or treatment planning methods

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