Improving Classification Performance for a Novel Imbalanced Medical Dataset Using SMOTE Method

In recent decades, machine learning algorithms have been used in different fields; one of the most used fields is the health sector. Biomedical data are usually extensive in size, and very hard to be analyzed and interpreted by humans. For this purpose, machine learning models are used to extract hidden patterns from data. In this paper, we aim to analyze, diagnose, and classify diabetes patients using six machine learning algorithms for a new real diabetes dataset. The newly created dataset, called ZADA, is obtained from medical records of about 7000 patients in Zakho city, Kurdistan Region of Iraq. However, our new dataset is imbalanced, which means one class is the minority and the other one is the majority. Class imbalance is a challenging problem in classification, especially in the two-class dataset. When class distributions are imbalanced, traditional machine learning methods often give low classification performance for unseen samples of the minority class. This is because the model tends to be strongly directed by the majority class. To overcome these problems, we first examine the impact of the imbalanced data on the classification performance and hence, use a resampling method to rebalance the data. Furthermore, we utilized three normalization techniques as a preprocessing step to further improve the performance of machine learning algorithms' performance. Therefore, we propose a classification analysis based on the three normalization methods along with the resampling (SMOTE) method to tackle the class imbalance problem. Various experiments are conducted to find the best algorithm with the best performance according to the distribution of minority classes. Results show that the resampling method and the normalization techniques had a positive effect on classification model performance.