Early stage diabetes prediction by features selection with metaheuristic methods
| dc.contributor.author | Özmen, T | |
| dc.contributor.author | Kuzu, Ü | |
| dc.contributor.author | Kocyigit, Y | |
| dc.contributor.author | Sarnel, H | |
| dc.date.accessioned | 2024-07-18T11:39:36Z | |
| dc.date.available | 2024-07-18T11:39:36Z | |
| dc.description.abstract | Diabetes is a metabolic disease that is common worldwide. The number of people suffering from diabetes is expected to increase every year around the world. This means a negative impact on both the comfort of life of individuals and the health system. In this respect, it is important to diagnose the disease at an early stage. The high dimensionality of the data used for diagnostic purposes has a negative effect on the cost and time of the calculation. To avoid this, it is important to select the most valuable features for diagnosis. In this study, feature selection was made using Salp Swarm Algorithm, Artificial Bee Colony Algorithm, Whale Optimization Algorithm and Ant Colony Algorithm using the samples in the UCI (UCI Machine Learning Repository) data store. In order to evaluate the selected features, accuracy, sensitivity and specificity parameters were calculated using k-Nearest Neighborhood (KNN), Naive Bayes (NB), Support Vector Machine (SVM) and Artificial Neural Networks (ANN) methods. In the calculations for the probability of having diabetes, an accuracy rate of 99.04% was obtained with the k-Nearest Neighborhood method. | |
| dc.identifier.issn | 1300-7009 | |
| dc.identifier.other | 2147-5881 | |
| dc.identifier.uri | http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/1767 | |
| dc.language.iso | English | |
| dc.publisher | PAMUKKALE UNIV | |
| dc.subject | SALP SWARM ALGORITHM | |
| dc.subject | DIAGNOSIS | |
| dc.title | Early stage diabetes prediction by features selection with metaheuristic methods | |
| dc.type | Article |