Data mining approach for accelerating the classification accuracy of cardiotocography

Objective: The objective of current study is to increase the classification accuracy of learning algorithms over cardiotocography data by applying preprocessing technique. Due to the diversity of sources, large amount of data is being generated and also has various problems including mislabeled data, missing values, noise, high dimensionality and imbalanced class labels. Method: In this study, we suggested a technique to handle imbalanced data to increase the classification performance of various lazy learners, rule based induction models and tree based models. We used Symmetric Minority Over Sampling Technique (SMOTE) on real dataset to accelerate the performance of various classifiers. We identified that primary dataset is suffering with imbalanced problem, which means the most of the records belong to same class label. Prediction of imbalanced data is biased towards the class with majority instances. To overcome this problem, dataset has to be balanced. Results: As a result of the suggested method the performance of classification algorithms are increased. The obtained result show that majority of classification techniques performed better over balanced dataset when compared with imbalanced dataset. Conclusion: Classification performance over balanced dataset has recorded improved performance than imbalanced dataset after applying the SMOTE.