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 classific...
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| Format: | Thesis Book |
| Language: | English |
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| 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 impled 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. |
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| Physical Description: | xviii,257 leaves; 31cm. |
| Bibliography: | Includes bibliographical references (leaves 192-208) |