| 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 imple~ented with the "deep learning method using deep
neural networks approach. The classifiers have been chosen to be combined are
Sequential Minimal Optimisation (SMO), Naive Bayes (NB), Random Forest (RF),
Decision Tree (148), and instance-based learning with parameter k (IBk). The
following classifiers are used here because they are used by most researchers that
proven bytheir pre-vious performance. The proposed method lias 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 bas~d 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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