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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Abdu Masanawa Sagir (USM) | |
| dc.contributor.author | Saratha Sathasivam (USM) | |
| dc.date.accessioned | 2023-11-06T03:15:25Z | - |
| dc.date.available | 2023-11-06T03:15:25Z | - |
| dc.date.issued | 2017-01 | |
| dc.identifier.issn | 0128-7680 | |
| dc.identifier.other | ukmvital:116472 | |
| dc.identifier.uri | https://ptsldigital.ukm.my/jspui/handle/123456789/579128 | - |
| dc.description | Adaptive Neuro Fuzzy Inference System (ANFIS) is among the most efficient classification and prediction modelling techniques used to develop accurate relationship between input and output parameters in different processes. This paper reports the design and evaluation of the classification performances of two discrete Adaptive Neuro Fuzzy Inference System models, ANFIS Matlab’s built-in model (ANFIS Adaptive Neuro Fuzzy Inference System (ANFIS) is among the most efficient classification and prediction modelling techniques used to develop accurate relationship between input and output parameters in different processes. This paper reports the design and evaluation of the classification performances of two discrete Adaptive Neuro Fuzzy Inference System models, ANFIS Matlab’s built-in model (ANFIS LSGD) and a newly ANFIS model with Levenberg-Marquardt algorithm (ANFIS_LSLM). Major steps were performed, which included classification using grid partitioning method, the ANFIS trained with least square estimates and Adaptive Neuro Fuzzy Inference System (ANFIS) is among the most efficient classification and prediction modelling techniques used to develop accurate relationship between input and output parameters in different processes. This paper reports the design and evaluation of the classification performances of two discrete Adaptive Neuro Fuzzy Inference System models, ANFIS Matlab’s built-in model (ANFIS LSGD) and a newly ANFIS model with Levenberg-Marquardt algorithm (ANFIS_LSLM). Major steps were performed, which included lassification using grid partitioning method, the ANFIS trained with least square estimates and backpropagation gradient descent method, as well as the ANFIS trained with Levenberg-Marquardt algorithm using finite difference technique for computation of a Jacobian matrix. The proposed ANFIS_LSLM model predicts the degree of patient’s heart disease with better, reliable and more accurate results. This is due to its new feature of index membership function that determines the unique membership functions in an ANFIS structure, which indexes them into a row-wise vector. In addition, an attempt was also done to specify the effectiveness of the model’s performance measuring accuracy, sensitivity and specificity. A comparison of the two models in terms of training and testing with the Statlog-Cleveland Heart Disease dataset have also been done. backpropagation gradient descent method, as well as the ANFIS trained with Levenberg-Marquardt algorithm using finite difference technique for computation of a Jacobian matrix. The proposed ANFIS_LSLM model predicts the degree of patient’s heart disease with better, reliable and more accurate results. This is due to its new feature of index membership function that determines the unique membership functions in an ANFIS structure, which indexes them into a row-wise vector. In addition, an attempt was also done to specify the effectiveness of the model’s performance measuring accuracy, sensitivity and specificity. A comparison of the two models in terms of training and testing with the Statlog-Cleveland Heart Disease dataset have also been done. LSGD) and a newly ANFIS model with Levenberg-Marquardt algorithm (ANFIS_LSLM). Major steps were performed, which included classification using grid partitioning method, the ANFIS trained with least square estimates and backpropagation gradient descent method, as well as the ANFIS trained with Levenberg-Marquardt algorithm using finite difference technique for computation of a Jacobian matrix. The proposed ANFIS_LSLM model predicts the degree of patient’s heart disease with better, reliable and more accurate results. This is due to its new feature of index membership function that determines the unique membership functions in an ANFIS structure, which indexes them into a row-wise vector. In addition, an attempt was also done to specify the effectiveness of the model’s performance measuring accuracy, sensitivity and specificity. A comparison of the two models in terms of training and testing with the Statlog-Cleveland Heart Disease dataset have also been done. | |
| dc.language.iso | en | |
| dc.publisher | Universiti Putra Malaysia Press | |
| dc.relation.haspart | Pertanika Journals | |
| dc.relation.uri | http://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-25-1-1 | |
| dc.rights | UKM | |
| dc.subject | Adaptive neuro fuzzy inference system | |
| dc.subject | Classification | |
| dc.subject | Grid Partitioning Method | |
| dc.subject | Levenberg- Marquardt algorithm | |
| dc.subject | Prediction | |
| dc.title | A novel adaptive neuro fuzzy inference system based classification model for heart disease prediction | |
| dc.type | Journal Article | |
| dc.format.volume | 25 | |
| dc.format.pages | 43-56 | |
| dc.format.issue | 1 | |
| Appears in Collections: | Journal Content Pages/ Kandungan Halaman Jurnal | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| ukmvital_116472+Source01+Source010.PDF | 492.88 kB | Adobe PDF |  View/Open |
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