Improving Cardiovascular Disease Prediction by Integrating Imputation, Imbalance Resampling, and Feature Selection Techniques into Machine Learning Model

Cardiovascular disease (CVD) is the leading cause of death worldwide. Primary prevention is by early prediction of the disease onset. Using laboratory data from the National Health and Nutrition Examination Survey (NHANES) in 2017-2020 timeframe (N= 7.974), we tested the ability of machine learning (ML) algorithms to classify individuals at risk. The ML models were evaluated based on their classification performances after comparing four imputation, three imbalance resampling, and three feature selection techniques.Due to its popularity, we utilized decision tree (DT) as the baseline. Integration of multiple imputation by chained equation (MICE) and synthetic minority oversampling with Tomek link down-sampling (SMOTETomek) into the model improved the area under the curve-receiver operating characteristics (AUC-ROC) from 57% to 83%. Applying simultaneous perturbation feature selection and ranking (spFSR) reduced the feature predictors from 144 to 30 features and the computational time by 22%. The best techniques were applied to six ML models, resulting in Xtreme gradient boosting (XGBoost) achieving the highest accuracy of 93% and AUC-ROC of 89%.The accuracy of our ML model in predicting CVD outperforms those from previous studies. We also highlight the important causes of CVD, which might be investigated further for potential effects on electronic health records.

Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks

Anomaly detection systems, also known as intrusion detection systems (IDSs), continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system’s performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i) performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii) employs logistic regression and extreme gradient boosting techniques for classification; (iii) introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv) uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system