2020_A Multi-Classifier Method Based On Deep Learning Approach For Diseases Classification
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| country | Malaysia |
| date | 2020-06-21 |
| format | General Document |
| id | 16170 |
| institution | UniSZA |
| originalfilename | 16170_316c937b80db7f8.pdf |
| person | Rosaida Binti Rosly |
| recordtype | oai_dc |
| resourceurl | https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16170 |
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| spelling | 16170 https://intelek.unisza.edu.my/intelek/pages/view.php?ref=16170 https://intelek.unisza.edu.my/intelek/pages/search.php?search=!collection3 General Document Malaysia Library Staff (Top Management) Library Staff (Management) Library Staff (Support) Terengganu Faculty of Informatics & Computing English application/pdf 1.5 Server storage Scanned document Universiti Sultan Zainal Abidin UniSZA Private Access UNIVERSITI SULTAN ZAINAL ABIDIN SAMBox 2.3.4; modified using iTextSharp™ 5.5.10 ©2000-2016 iText Group NV (AGPL-version) 279 Copyright©PWB2025 Rosaida Binti Rosly Deep learning 2020-06-21 16170_316c937b80db7f8.pdf Machine Learning 2020_A Multi-Classifier Method Based On Deep Learning Approach For Diseases Classification Accurate prediction of diseases classification is vital in the medical-related field. Misclassification of diseases would be detrimental as it may lead to the misdiagnosis and wrong treatment of patients. In medical fields, the most important performance measurement indicators for diseases classification are sensitivity, specificity, and accuracy. By using a single classifier, it is impossible to achieve optimal levels of sensitivity and accuracy. Thus, this research proposed a multi-classifier method based deep learning approach that aims to increase the sensitivity and accuracy of diseases classification. Five disease datasets (Breast Cancer Wisconsin, Hepatitis, Pima Indians Diabetes, Parkinson‟s, and Indian Liver Patient) have been chosen as an application field to examine the proposed multi-classifier approach in achieving high accuracy. Multiple stages are involved in this research, including the generation of predictive methods, selection of methods using single classification, application of fusion classification between different classifiers via a combination of two or more classifiers, followed by the selection of the fusion output that has the highest accuracy before combining it with other classifiers. Lastly, the combination of prediction class by relevant classifier has been implemented with the deep learning method using deep neural networks approach. The classifiers have been chosen to be combined are Sequential Minimal Optimisation (SMO), Naïve Bayes (NB), Random Forest (RF), Decision Tree (J48), and instance-based learning with parameter k (IBk). The following classifiers are used here because they are used by most researchers that proven by their previous performance. The proposed method has recorded a higher accuracy than single and multi-classifier methods. The result has proved that it has been improved using the proposed method and shows a comparable result from the previous research. The highest improvements of accuracy for datasets were Breast Cancer Wisconsin (96.63%), Hepatitis (92.50%), Pima Indians Diabetes (80.34%), Parkinson (87.69%), and Indian Liver Patient (74.79%). Based on five disease datasets studied, the combination of relevant classifiers based on deep learning approach has been found can increase the accuracy of the dataset in classifying the different disease. Mostly the dataset was improved in terms of accuracy using the proposed method than other methods such as single ones and multi-classifier method. Overall, our proposed method better in terms of considering accuracy, sensitivity, and specificity. Dissertations, Academic Deep Learning Multi-Classifier Systems Thesis |
| spellingShingle | 2020_A Multi-Classifier Method Based On Deep Learning Approach For Diseases Classification |
| state | Terengganu |
| subject | Deep learning Dissertations, Academic |
| summary | Accurate prediction of diseases classification is vital in the medical-related field. Misclassification of diseases would be detrimental as it may lead to the misdiagnosis and wrong treatment of patients. In medical fields, the most important performance measurement indicators for diseases classification are sensitivity, specificity, and accuracy. By using a single classifier, it is impossible to achieve optimal levels of sensitivity and accuracy. Thus, this research proposed a multi-classifier method based deep learning approach that aims to increase the sensitivity and accuracy of diseases classification. Five disease datasets (Breast Cancer Wisconsin, Hepatitis, Pima Indians Diabetes, Parkinson‟s, and Indian Liver Patient) have been chosen as an application field to examine the proposed multi-classifier approach in achieving high accuracy. Multiple stages are involved in this research, including the generation of predictive methods, selection of methods using single classification, application of fusion classification between different classifiers via a combination of two or more classifiers, followed by the selection of the fusion output that has the highest accuracy before combining it with other classifiers. Lastly, the combination of prediction class by relevant classifier has been implemented with the deep learning method using deep neural networks approach. The classifiers have been chosen to be combined are Sequential Minimal Optimisation (SMO), Naïve Bayes (NB), Random Forest (RF), Decision Tree (J48), and instance-based learning with parameter k (IBk). The following classifiers are used here because they are used by most researchers that proven by their previous performance. The proposed method has recorded a higher accuracy than single and multi-classifier methods. The result has proved that it has been improved using the proposed method and shows a comparable result from the previous research. The highest improvements of accuracy for datasets were Breast Cancer Wisconsin (96.63%), Hepatitis (92.50%), Pima Indians Diabetes (80.34%), Parkinson (87.69%), and Indian Liver Patient (74.79%). Based on five disease datasets studied, the combination of relevant classifiers based on deep learning approach has been found can increase the accuracy of the dataset in classifying the different disease. Mostly the dataset was improved in terms of accuracy using the proposed method than other methods such as single ones and multi-classifier method. Overall, our proposed method better in terms of considering accuracy, sensitivity, and specificity. |
| title | 2020_A Multi-Classifier Method Based On Deep Learning Approach For Diseases Classification |
| title_full | 2020_A Multi-Classifier Method Based On Deep Learning Approach For Diseases Classification |
| title_fullStr | 2020_A Multi-Classifier Method Based On Deep Learning Approach For Diseases Classification |
| title_full_unstemmed | 2020_A Multi-Classifier Method Based On Deep Learning Approach For Diseases Classification |
| title_short | 2020_A Multi-Classifier Method Based On Deep Learning Approach For Diseases Classification |
| title_sort | 2020_a multi-classifier method based on deep learning approach for diseases classification |