Name
Title | ||
|---|---|---|
Identifying β-thalassemia carriers using a data mining approach: The case of the Gaza Strip, Palestine. |
Abstract | ||
|---|---|---|
•We propose an automatic detection model of beta-thalassemia carriers based on a hybrid data mining approach.•A novel and real dataset from Gaza Strip is introduced and studied.•The highly imbalanced distribution in the dataset is solved by SMOTE oversampling.•The proposed model will support medical decisions by identifying beta-thalassemia carriers especially in countries with limited recourses or poor health services. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.artmed.2018.04.009 | Artificial Intelligence in Medicine |
Keywords | Field | DocType |
Thalassemia,Data mining,Classification,SMOTE,Oversampling,Imbalance,Medical dataset | Thalassemia,Data mining,Decision tree,Naive Bayes classifier,Oversampling,Computer science,Multilayer perceptron,Classifier (linguistics),Artificial neural network,Beta thalassemia | Journal |
Volume | ISSN | Citations |
88 | 0933-3657 | 1 |
PageRank | References | Authors |
0.36 | 20 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alaa S. AlAgha | 1 | 1 | 0.36 |
| Hossam Faris | 2 | 761 | 38.48 |
| Bassam Hammo | 3 | 55 | 6.46 |
| Ala' M. Al-Zoubi | 4 | 221 | 9.83 |