An improved artificial neural network based model for prediction of late onset heart failure
Background and Objective: The present study aims to present an artificial neural network (ANN)-based model for prediction of Late Onset Heart Failure (LOHF) in patients, with no previous Heart Failure (HF) history, who experienced non-fatal, first-ever Acute Myocardial Infarction (AMI) without previo...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Marsland Press
2012
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/13846/ http://psasir.upm.edu.my/id/eprint/13846/1/An%20improved%20artificial%20neural%20network%20based%20model%20for%20prediction%20of%20late%20onset%20heart%20failure.pdf |
| _version_ | 1848842227280773120 |
|---|---|
| author | Salari, Nader Shohaimi, Shamarina Najafi, Farid Nallappan, Meenakshii Karishnarajah, Isthrinayagy |
| author_facet | Salari, Nader Shohaimi, Shamarina Najafi, Farid Nallappan, Meenakshii Karishnarajah, Isthrinayagy |
| author_sort | Salari, Nader |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Background and Objective: The present study aims to present an artificial neural network (ANN)-based model for prediction of Late Onset Heart Failure (LOHF) in patients, with no previous Heart Failure (HF) history, who experienced non-fatal, first-ever Acute Myocardial Infarction (AMI) without previous history of heart failure. Methods: Two models of multilayer perceptron (MLP) and Radial Basis Function (RBF) neural network approaches based on decision support system were developed. The MLP model was used to optimize the predicting algorithm based on the conjugate gradients descent method. To design the RBF network, K-Means clustering technique was used to select the centers of RBFs, and k-nearest neighbourhood to define the spread and forward selection for determining the optimum number of RBFs. To assess the generalization of the network, K-fold cross-validation test was used. A total of 3,109 medical records containing 19 main clinical parameters were used to train and test the networks. Results: The findings indicate a reliable performance of the proposed system. The MLP based model yields a sensitivity, specificity, and an area under the receiver/relative operating characteristic (ROC) curve (AUC) of 87.1%, 90%, and 0.887 ± 0.02, respectively. However, the RBF network shows the above parameters as 84.4%, 94.3%, and 0.905 ± 0.017, respectively. Conclusions: The proposed intelligence system achieved a high degree of diagnostic accuracy (92.9% for MLP and 93.7% for RBF) indicating its high efficiency for clinical diagnosis of LOHF. |
| first_indexed | 2025-11-15T07:55:47Z |
| format | Article |
| id | upm-13846 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T07:55:47Z |
| publishDate | 2012 |
| publisher | Marsland Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-138462018-08-14T08:48:54Z http://psasir.upm.edu.my/id/eprint/13846/ An improved artificial neural network based model for prediction of late onset heart failure Salari, Nader Shohaimi, Shamarina Najafi, Farid Nallappan, Meenakshii Karishnarajah, Isthrinayagy Background and Objective: The present study aims to present an artificial neural network (ANN)-based model for prediction of Late Onset Heart Failure (LOHF) in patients, with no previous Heart Failure (HF) history, who experienced non-fatal, first-ever Acute Myocardial Infarction (AMI) without previous history of heart failure. Methods: Two models of multilayer perceptron (MLP) and Radial Basis Function (RBF) neural network approaches based on decision support system were developed. The MLP model was used to optimize the predicting algorithm based on the conjugate gradients descent method. To design the RBF network, K-Means clustering technique was used to select the centers of RBFs, and k-nearest neighbourhood to define the spread and forward selection for determining the optimum number of RBFs. To assess the generalization of the network, K-fold cross-validation test was used. A total of 3,109 medical records containing 19 main clinical parameters were used to train and test the networks. Results: The findings indicate a reliable performance of the proposed system. The MLP based model yields a sensitivity, specificity, and an area under the receiver/relative operating characteristic (ROC) curve (AUC) of 87.1%, 90%, and 0.887 ± 0.02, respectively. However, the RBF network shows the above parameters as 84.4%, 94.3%, and 0.905 ± 0.017, respectively. Conclusions: The proposed intelligence system achieved a high degree of diagnostic accuracy (92.9% for MLP and 93.7% for RBF) indicating its high efficiency for clinical diagnosis of LOHF. Marsland Press 2012 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/13846/1/An%20improved%20artificial%20neural%20network%20based%20model%20for%20prediction%20of%20late%20onset%20heart%20failure.pdf Salari, Nader and Shohaimi, Shamarina and Najafi, Farid and Nallappan, Meenakshii and Karishnarajah, Isthrinayagy (2012) An improved artificial neural network based model for prediction of late onset heart failure. Life Science Journal, 9 (4). pp. 3684-3689. ISSN 1097-8135; ESSN: 2372-613X http://www.lifesciencesite.com/lsj/life0904/ 10.7537/marslsj090412.546 |
| spellingShingle | Salari, Nader Shohaimi, Shamarina Najafi, Farid Nallappan, Meenakshii Karishnarajah, Isthrinayagy An improved artificial neural network based model for prediction of late onset heart failure |
| title | An improved artificial neural network based model for prediction of late onset heart failure |
| title_full | An improved artificial neural network based model for prediction of late onset heart failure |
| title_fullStr | An improved artificial neural network based model for prediction of late onset heart failure |
| title_full_unstemmed | An improved artificial neural network based model for prediction of late onset heart failure |
| title_short | An improved artificial neural network based model for prediction of late onset heart failure |
| title_sort | improved artificial neural network based model for prediction of late onset heart failure |
| url | http://psasir.upm.edu.my/id/eprint/13846/ http://psasir.upm.edu.my/id/eprint/13846/ http://psasir.upm.edu.my/id/eprint/13846/ http://psasir.upm.edu.my/id/eprint/13846/1/An%20improved%20artificial%20neural%20network%20based%20model%20for%20prediction%20of%20late%20onset%20heart%20failure.pdf |