Improving Accuracy of Imbalanced Clinical Data Classification Using Synthetic Minority Over-Sampling Technique
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...
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| Main Authors: | , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://eprints.unisza.edu.my/1819/1/FH03-FIK-20-39681.pdf http://eprints.unisza.edu.my/1819/ |
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| Institution: | Universiti Sultan Zainal Abidin |
| Language: | English |
| Summary: | 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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