MALARIA SEVERITY CLASSIFICATION THROUGH JORDAN-ELMAN NEURAL NETWORK BASED ON FEATURES EXTRACTED FROM THICK BLOOD SMEAR
| Autor: | Sameem Abdul Kareem, Adamu Abubakar, Ibrahim Abdullahi Shehu Bashir Musa Umar, Chiroma Haruna, Gadam Ahmad, Mukhtar Fatihu, Garba Abdulmumini, Tutut Herawan |
|---|---|
| Rok vydání: | 2015 |
| Předmět: |
Mean squared error
Artificial neural network business.industry Computer science General Neuroscience Confusion matrix medicine.disease Machine learning computer.software_genre Support vector machine Artificial Intelligence Hardware and Architecture Linear regression medicine Artificial intelligence Performance improvement business computer Software Malaria Reliability (statistics) |
| Zdroj: | Neural Network World. 25:565-584 |
| ISSN: | 2336-4335 1210-0552 |
| DOI: | 10.14311/nnw.2015.25.028 |
| Popis: | This article presents an alternative approach useful for medical prac- titioners who wish to detect malaria and accurately identify the level of severity. Malaria classiers are usually based on feed forward neural networks. In this study, the proposed classier is developed based on the Jordan-Elman neural networks. Its performance is evaluated using a receiver-operati characteristic curve, sensitiv- ity, specicity, positive predictive value, negative predictive value, confusion matrix, mean square error, determinant coefficient, and reliability. The effectiveness of the classier is compared to a support vector machine and multiple regression models. The results of the comparative analysis demonstrate a superior performance level of the Jordan-Elman neural network model. Further comparison of the classier with previous literature indicates performance improvement over existing results. The Jordan-Elman neural networks classier can assist medical practitioners in the fast detection of malaria and determining its severity, especially in tropical and subtropical regions where cases of malaria are prevalent. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|