Application of Support Vector Machine for Class Imbalance Learning to Predict Anticancer Compounds of Medicinal Plants in West Sulawesi

Abstract

Indonesian medicinal plants, such as turmeric and soursop, have shown promising anticancer properties through their bioactive compounds, like curcumin and extracts from soursop. Despite many extensive studies on medicinal plants in Indonesia, research revealing the activity of natural products in West Sulawesi is still limited, and the studies focus mainly on ethnobotanical research. In this work, we propose a machine-learning approach to predict the anticancer activity of compounds in medicinal plants in West Sulawesi by leveraging high throughput-screening data, especially molecular information from a public database. We applied Support Vector Machine (SVM) with five sampling techniques to address data imbalance. We also evaluated the performance in selecting the best combination in handling class imbalance learning in our dataset. The result shows that undersampling and ADSYN methods can improve the prediction of anticancer activity. Based on the two methods of balancing data, we have ten potential anticancer compounds from three medicinal plants in West Sulawesi.