HSVNN: an efficient medical data classification using dimensionality reduction combined with hybrid support vector neural network
Journal of Supercomputing, ISSN: 1573-0484, Vol: 78, Issue: 13, Page: 15439-15462
2022
- 6Citations
- 8Captures
Metric Options: Counts1 Year3 YearSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
Early diagnosis and therapy are the most essential strategies to prevent deaths from diseases, such as cancer, brain tumors, and heart diseases. In this regard, information mining and artificial intelligence approaches have been valuable tools for providing useful data for early diagnosis. However, high-dimensional data can be challenging to examine, practically difficult to visualize, and costly to measure and store. Transferring a high-dimensional portrayal of the data to a lower-dimensional one without losing important information is the focal issue of dimensionality reduction. Therefore, in this study, dimensionality reduction-based medical data classification is presented. The proposed methodology consists of three modules: pre-processing, dimension reduction using an adaptive artificial flora (AAF) algorithm, and classification. The important features are selected using the AAF algorithm to reduce the dimension of the input data. From the results, a dimension-reduced dataset is obtained. The reduced data are then fed as input to the hybrid classifier. A hybrid support vector neural network is proposed for classification. Finally, the effectiveness of the proposed method is analyzed in terms of different metrics, namely accuracy, sensitivity, and specificity. The proposed method is implemented in MATLAB.
Bibliographic Details
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know