Wachiranun Sirikul and Trasapong Thaiupathump

Published in Data Science and Engineering (DSE) Record 2021 Vol. 2 No. 1 pp. 14-18



Thailand is a middle-income country where the road traffic injury crisis has been one of the most serious public health concerns. Currently, the machine learning (ML) algorithms are widely used for public health predictive analyt-ics. Therefore, we developed the Multi-layer perceptron (MLP) classifier from the road traffic accident driver data in Thailand that aim to classify a high-risk driver who had severe injuries from road traffic accidents. Howev-er, the imbalanced data was a typical problem in public health data and also caused an “accuracy paradox” that the model intended to predict a majority class. Accurately detecting minority class was important especially in the public health data because it was associated with high impact events and se-rious adverse outcomes. Since the imbalanced data is unavoidable according to the nature of public health data. The rebalanced strategies or other data approaches were applied to encounter this problem. Subsequently, the over-sampling techniques were significantly improved discrimination performanc-es of models comparing with under-sampling or without rebalancing ap-proach.