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Prologue
This page is a summary of my paper at Konferensi Nasional Matematika 2014 Institut Teknologi Surabaya (ITS). I apologize for not being able to include the source code in this page because of technical problem.
To find out whether the patient has brain glioma or brain infection, an examination from the radiology department is needed. Methods in the examination can use Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS).
Magnetic Resonance Spectroscopy is an additional technique of MRI examination which is included in functional imaging because it produces information on tissu biochemical characterization.
Brain glioma or brain infection needs to be classified because of the difference in action. If the patient has a brain glioma, surgery is needed. If the patient have brain infection, it is enought to give the drug according to the cause of the brain infection.
The Support Vector Machine (SVM) is used to classify brain gliomas or brain infections based on MRS results data. The data used comes from MRS from Pantai Indah Kapuk Hospital, Jakarta.
Related research regarding the classification of glioma (in this case Astrocytoma is a specific form of glioma) has been done with method Fuzzy C-means, Possiblistic C-means, Spherical K-means, Online Spherical K-means, Adaptive Boosting and K-Nearest Neighbor. In this page, the method Support Vector Machine (SVM) is used to classify brain gliomas or brain infections.
Support Vector Machine
Support Vector Machine (SVM) is an algorithm that belongs to machine learning. The most important of this algorithm is to find a plane that can separate data with the optimal separation distance.
With this one plane, it can be seen that Support Vector Machine is a two-class classification algorithm.
Below is the summary of the Support Vector Machine Algorithm
Conclusion
Based on the experiments conducted, the Support Vector Machine with Kernel Gaussin 0.2 is the best parameter for brain gliomas or brain infections classification. In this problem, the Support Vector Machine has the higher accuracy 97.8%. So it can be concluded that the Support Vector Machine method can be used for the classification of brain gliomas or brain infections.
Reference
Rahman, Luthfir. (2014). Klasifikasi Glioma Otak atau Infeksi Otak Menggunakan K-Nearest Neighbor dan AdaBoost. Depok: Universitas Indonesia.
Rahman, Luthfir, Rustam, Z. and Pandelaki, J. (2014). Klasifikasi Glioma Otak atau Infeksi Otak Menggunakan Support Vector Machine (SVM).
https://slideplayer.com/slide/4044039/ . Accessed 24 January 2023
Fikri, A., Rustam, Z. and Pandelaki, J., Brain Cancer (Astrocytoma) Clustering Menggunakan Metode Fuzzy C-Means, Prosiding Seminar Nasional Matematika FMIPA UI, (pp. 271–277). Depok.
Krismanti, A., Rustam, Z. and Pandelaki, J., Aplikasi Spherical K-Means pada Pengklasfikasian Brain Cancer, Prosiding Seminar Nasional Matematika 2010, (pp. 293–296). Depok.
Wibowo, A. P., Rustam, Z. and Pandelaki, J., Clustering Brain Cancer menggunakan Possibilitic C-Means, Prosiding Seminar Nasional Matematika 2010, (pp.289–292). Depok.