A Hybrid ROS-SVM Model for Detecting Target Multiple Drug Types

Nur Ramadhan - Institut Teknologi Telkom Purwokerto, Indonesia,
Azka Khoirunnisa - Telkom University, Bandung, Indonesia
Kurnianingsih Kurnianingsih - Politeknik Negeri Semarang, Indonesia
Takako Hashimoto - Chiba University of Commerce, Japan

Citation Format:

DOI: http://dx.doi.org/10.30630/joiv.7.3.1171


Misleading in determining the decision to use the target drug will be fatal, even to death. This study examines five pharmacological targets designated as types A, B, C, X, and Y. Early detection of misleading drug targeting will reduce the risk of death. This study aims to develop hybrid random oversampling techniques (ROS) and support vector machine (SVM) methods. The use of the oversampling technique in this study aims to balance classes in the dataset; due to the data collection in each class, there is a relatively large gap. This study applies five schemes to see which combination of models produces the highest accuracy. This study also uses five types of SVM kernels, linear, polynomial, gaussian, RBF, and sigmoid, combined with the ROS oversampling technique. Our proposed model combines the ROS oversampling technique with a linear SVM kernel. We evaluated the proposed model and resulted in an accuracy of 97% and compared it with several experiments, including the ROS technique with a sigmoid kernel which only resulted in 50% accuracy. It can be seen from the results obtained that the linear kernel is very adaptive to data types in the form of numeric and nominal compared to other kernels. The method proposed in this study can be applied to other medical problems. Future research can be carried out using a combination of other sampling techniques with deep learning-based methods on this issue.


Drug; random oversampling; support vector machine; balancing data

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