• Raghavendra S.
• Santosh Kumar J.
• Raghavendra B K

Diagnosis of Diabetes disease at beginning stage is important for healthier treatment. In today’s scenario equipment’s like sensors are used for discovery of infections. Accurate classification techniques are necessary for automatic detection of disease samples. This study utilizes data mining techniques for classification of Diabetes patients. Neural network algorithm was implemented for classification using R platform. Classification and prediction of medical datasets poses real challenges in Data Mining. To deal with these challenges and Artificial Neural Network (ANN) is commonly used. LR enables us to examine the relationship between a categorical outcome and a set of descriptive variables. LR explains that there can be one or more self-governing variables that can establish the problem outcome. ANN resembles the human brain and here the information is processed by simple elements called neurons and signals are transmitted between the neuron from the experimental results it is identified that for Diabetes dataset NN with 10 fold using percentage split prediction correctness of 84.52% is achieved. Artificial neural networks are considered as a field of artificial intelligence. It has also been applied in many disciplines, including biology, psychology, statistics, mathematics, medical science, and computer science. Feature selection is the process of choosing a small subset of features that is sufficient to describe the target model. In this research work an attempt has been made to evaluate neural network model with feature selection methods such as forward selection and backward elimination using cross validation sampler and percentage split on publicly available medical datasets. The classification accuracy is used to measure the performance of the model. From the experimental results it is confirmed that the neural network model with backward elimination feature selection method using percentage split gives more efficient result.

Artificial Neural Network, Feature Selection Method, Percentage Split, Cross validation sampler

"EVALUATING THE PERFORMANCE OF NEURAL NETWORK USING FEATURE SELECTION METHODS ON PIMA INDIAN DIABETES DATASET", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 12, page no.268-273, December-2018, Available :http://www.jetir.org/papers/JETIR1812C40.pdf

"EVALUATING THE PERFORMANCE OF NEURAL NETWORK USING FEATURE SELECTION METHODS ON PIMA INDIAN DIABETES DATASET", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 12, page no. pp268-273, December-2018, Available at : http://www.jetir.org/papers/JETIR1812C40.pdf