TY - JOUR
T1 - Feature optimization using Backward Elimination and Support Vector Machines (SVM) algorithm for diabetes classification
AU - Maulidina, F.
AU - Rustam, Z.
AU - Hartini, S.
AU - Wibowo, V. V.P.
AU - Wirasati, I.
AU - Sadewo, W.
N1 - Funding Information:
This research is fully supported financially by The Indonesian Ministry of Research, Technology, with a KEMENRISTEK/BRIM PDUPT 2021 research grant scheme.
Publisher Copyright:
© Published under licence by IOP Publishing Ltd.
PY - 2021/3/29
Y1 - 2021/3/29
N2 - Diabetes is a disease that occurs when the blood glucose level is higher than normal and also leads to health problems. Early and accurate diagnosis needs to be carried out on individuals affected by this disease. Furthermore, excellent treatment needs to be provided to prevent worse situations. Some studies have used several machine learning methods to diagnose diabetes. Furthermore, in this study, the Backward Elimination and Support Vector Machine (SVM) algorithm was used to classify the PIMA Indians diabetes dataset. It consisted of 268 diabetic and 500 non-diabetic patients with eight attributes. Backward Elimination is a feature selection method used to remove irrelevant features based on the linear regression model. Using this method, the right features for the model was expected. This method has some advantages which include increasing training time, decreasing complexity and improving performance and accuracy. Therefore, the performance of SVM improved. Based on the experiments, it was discovered that by combining feature selection algorithm (backward elimination) and SVM, the highest accuracy obtained was 85.71% using 90% data training. Therefore, it was concluded that Backward Elimination combined with SVM algorithm is an excellent method to classify diabetes by using the PIMA Indians diabetes dataset.
AB - Diabetes is a disease that occurs when the blood glucose level is higher than normal and also leads to health problems. Early and accurate diagnosis needs to be carried out on individuals affected by this disease. Furthermore, excellent treatment needs to be provided to prevent worse situations. Some studies have used several machine learning methods to diagnose diabetes. Furthermore, in this study, the Backward Elimination and Support Vector Machine (SVM) algorithm was used to classify the PIMA Indians diabetes dataset. It consisted of 268 diabetic and 500 non-diabetic patients with eight attributes. Backward Elimination is a feature selection method used to remove irrelevant features based on the linear regression model. Using this method, the right features for the model was expected. This method has some advantages which include increasing training time, decreasing complexity and improving performance and accuracy. Therefore, the performance of SVM improved. Based on the experiments, it was discovered that by combining feature selection algorithm (backward elimination) and SVM, the highest accuracy obtained was 85.71% using 90% data training. Therefore, it was concluded that Backward Elimination combined with SVM algorithm is an excellent method to classify diabetes by using the PIMA Indians diabetes dataset.
UR - http://www.scopus.com/inward/record.url?scp=85103897338&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/1821/1/012006
DO - 10.1088/1742-6596/1821/1/012006
M3 - Conference article
AN - SCOPUS:85103897338
SN - 1742-6588
VL - 1821
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012006
T2 - 6th International Conference on Mathematics: Pure, Applied and Computation, ICOMPAC 2020
Y2 - 24 October 2020
ER -