An Assessment of Classification with Hybrid Methodology for Neural Network Classifier against different Classifiers

Neeraj Bhargava, Abhishek Kumar, Pramod Singh Rathore, Aakanksha Jain


Introduction: The research An Assessment of Classification with Hybrid Methodology for Neural Network Classifier against different Classifiers focuses on working efficiency of different classifiers and comparative results. Authentic & dynamic datasets are considered in order to get at specific meaning full results and was conducted at M.D.S University Ajmer in 2017.

Methods: According to the Indian Heart Association, almost half of heart attack victim Indian citizen in enduring this disease by age of 50 years and around one-fourth of all heart-attack happen in Indian occur under 40 years of age. In the considerable amount of cases, lack of cognition is the major reason for the delay in treatment.

Results: So we are giving a prototype definition of a Hybrid Technology of Analyzing Accuracy measure of neural network classifiers with Bayesian classifiers, Rules-based classifiers and tree-based classifiers on Diagnosis of Heart Disease dataset.

Conclusions: This Methodology will distinguish the functioning, as well as the execution results of these data mining classifiers

Originality: The work consider large dataset and different approach has been applied classifier function naïve Bayes algorithm, is taken as base algorithm and all other algorithms like, Rule classification algorithm Ridor and Trees Classifier algorithm Simple Cart are used for comparison purpose, in order to predict angiographic disease status of patient.

Limitations: As the proposed work is new implementation it is needed to be implemented on different dynamic datasets to get assured for result accuracy and efficiency of proposed method.


Naive bayes classifier; Simple cart; Rule classification; Trees Classifier


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