Abstract
A Comparative Study of Legendre Neural Network and Chebyshev Functional Link Artificial Neural Network for Diabetes Data Classification - written by Swati Das published on 2020/06/19 download full article with reference data and citations
Highlights
The current area of interest and research work in the health care sector is the prevention of various diabetes-related diseases
Performance Metrics For analysing the results and performance metrics of Legendre Neural Network (LeNN) and Chebyshev Functional Link based ANN (CHFLANN) the f-measure and accuracy has been considered for training and testing of input dataset
Description In our proposed work to analyze the performance of LeNN and Chebyshev functional link artificial neural network (CFLANN), the PIMA Indian Diabetes database is divided into training and testing set of 512 and 256 attribute data respectively
Summary
The current area of interest and research work in the health care sector is the prevention of various diabetes-related diseases. Numerous data mining methods have been proposed and performance analysis has been done for the identification of major causes of diabetes when considering several data sets. In [1] a method of data mining considering an analytical problem of health care system in the New Orleans Area with 30,386 diabetic patients was performed with respective results. In [6] an approach has been established for the comparison analysis on Decision Tree, Multi-Layer Perceptron (MLP) and Chebyshev functional link artificial neural network (CFLANN) in terms of their classification accuracy and elapsed time for credit card fraud detection. With reference to the above proposed works and considering a new problem statement for diabetes data analysis, we have decided to compare the results for LeNN with respect to CHFLANN. We will calculate the mean square error curve for both the processes .The details for the proposed technique has been given below along with the appropriate mathematical equations
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