Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique

Mumtazimah, Mohamad and Mohd, F and Abdul Jalil, M and Noora, N.M.M and Ismail, S and Yahya, W.F.F (2019) Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique. In: 1st International Conference on Intelligent Cloud Computing, ICC 2019, 10-12 December 2019, Riyadh; Saudi Arabia.

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Abstract

Imbalanced datasets typically occur in many real applications. Resampling is one of the effective solutions due to producing a balanced class distribution. Synthetic Minority Over-sampling technique (SMOTE), an over-sampling technique is used in this study for dealing the imbalanced dataset by add the number of instances of a minority class. This technique is used to decrease the imbalance percentage of the dataset by generating new synthetic samples. Thus, a balanced training dataset is produced to replace the class imbalanced . The balanced datasets were obtained and trained with machine learning algorithms to diagnose the disease’s class. Through the experiment findings on the real-world datasets, oral cancer dataset and erythemato-squamous diseases dataset from the UCI machine learning datasets, an over-sampling method showed better results in clinical disease classification.

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