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In this paper, two real-world medical classification problems using electrocardiogram (ECG) and auscultatory blood pressure (Korotkoff) signals are examined. A total of nine machine learning models are applied to perform classification of the medical data sets. A number of useful performance metrics which include accuracy, sensitivity, specificity, as well as the area under the receiver operating characteristic curve are computed. In addition to the original data sets, noisy data sets are generated to evaluate the robustness of the classifiers against noise. The 10-fold cross validation method is used to compute the performance statistics, in order to ensure statistically reliable results pertaining to classification of the ECG and Korotkoff signals are produced. The outcomes indicate that while logistic regression models perform the best with the original data set, ensemble machine learning models achieve good accuracy rates with noisy data sets. [ABSTRACT FROM AUTHOR]

Highlights: [•] An ensemble of hybrid intelligent models is applied to condition monitoring tasks. [•] A review on condition monitoring of induction motors is presented. [•] The proposed model is able to learn incrementally and explain its predictions. [•] The results ascertain the usefulness of the model for condition monitoring tasks. [•] The knowledge base is presented as a decision tree for interpretation by users. [ABSTRACT FROM AUTHOR]

Highlights: [•] A hybrid intelligent system is proposed for medical data classification tasks. [•] The proposed system is able to learn incrementally and explain its predictions. [•] Benchmark medical data sets are used to evaluate the effectiveness of the system. [•] The results ascertain the usefulness of the system for medical decision support. [•] The knowledge base is presented as a decision tree for interpretation by users. [ABSTRACT FROM AUTHOR]