PERBANDINGAN BEBERAPA METODE KLASIFIKASI DALAM MEMPREDIKSI INTERAKSI FARMAKODINAMIK

  • Hasnita Hasnita Department of Statistics, IPB University, Indonesia
  • Farit Mochamad Afendi Department of Statistics, IPB University, Indonesia
  • Anwar Fitrianto Department of Statistics, IPB University, Indonesia
Keywords: binary logistic regression, drug-drug interaction, random forest, support vector machines

Abstract

One mechanism for Drug-Drug Interaction (DDI) is pharmacodynamic (PD) interactions. They are interactions by which the effects of a drug are changed by other drugs at the site of receptor. The interactions can be predicted based on Side Effects Similarity (SES), Chemical Similarity (CS) and Target Protein Connectedness (TPC). This study aims to find the best classification technique by first applying the scaling process, variable interaction, discretization and resampling technique. We used Random Forest, Support Vector Machines (SVM) and Binary Logistic Regression for the classification. Out the three classification methods, we found the SVM classification method produces the highest Area Under Cover (AUC) value compared to the other, which is 67.91%.

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Published
2020-02-28
How to Cite
Hasnita, H., Afendi, F., & Fitrianto, A. (2020). PERBANDINGAN BEBERAPA METODE KLASIFIKASI DALAM MEMPREDIKSI INTERAKSI FARMAKODINAMIK. Indonesian Journal of Statistics and Its Applications, 4(1), 11-21. https://doi.org/10.29244/ijsa.v4i1.328
Section
Articles