Open AccessImproving Neural Network Performance with Feature Selection Using Pearson Correlation Method for Diabetes Disease Detection
Author(s) -
April Firman Daru,
Mohammad Burhan Hanif,
Edi Wahyu Widodo
Publication year - 2021
Publication title -
juita : jurnal informatika/jurnal informatika
Language(s) - English
Resource type - Journals
eISSN - 2579-8901
pISSN - 2086-9398
DOI - 10.30595/juita.v9i1.9941
Subject(s) - pearson product moment correlation coefficient , artificial neural network , correlation , feature selection , computer science , artificial intelligence , data mining , feature (linguistics) , backpropagation , pattern recognition (psychology) , machine learning , statistics , mathematics , linguistics , philosophy , geometry
Diabetic or silent killer diseases are an alarming scourge for the world and are classed as serious diseases. In Indonesia, the increase in diabetics occurred by 2% in vulnerable times between 2013 to 2018. This affects all sectors, both medical services and the financial sector. The Neural Network method as a data mining algorithm is present to overcome the burden that arises as an early detection analysis of the onset of disease. However, Neural Network has slow training capabilities and can identify important attributes in the data resulting in a decrease in performance. Pearson correlation is good at handling data with mixed-type attributes and is good at measuring information between attributes and attributes with labels. With this, the purpose of this study will be to use the Pearson correlation method as a selection of features to improve neural network performance in diabetes detection and measure the extent of accuracy obtained from the method. The dataset used is diabetes data 130-US hospital UCI with a record number of 101767 and the number of attributes as many as 50 attributes. The results of this study found that Pearson correlation can improve neural network accuracy performance from 94.93% to 96.00%. As for the evaluation results on the AUC value increased from 0.8077 to 0.8246. Thus Pearson's Correlation algorithm can work well for feature selection on neural network methods and can provide solutions to improved diabetes detection accuracy.