Abstract
BackgroundAs per the 2017 WHO fact sheet, Coronary Artery Disease (CAD) is the primary cause of death in the world, and accounts for 31% of total fatalities. The unprecedented 17.6 million deaths caused by CAD in 2016 underscores the urgent need to facilitate proactive and accelerated pre-emptive diagnosis. The innovative and emerging Machine Learning (ML) techniques can be leveraged to facilitate early detection of CAD which is a crucial factor in saving lives. The standard techniques like angiography, that provide reliable evidence are invasive and typically very expensive and risky. In contrast, ML model generated diagnosis is non-invasive, fast, accurate and affordable. Therefore, it can be used as a supplement or precursor to the conventional methods. This research demonstrates the implementation of K Nearest Neighbor (k-NN) and Random Forest ML algorithms to achieve a targeted “At Risk” CAD classification using an emerging set of 35 cytokine biomarkers that are strongly indicative predictive variables that can be potential targets for therapy. To ensure better generalizability, mechanisms such as data balancing, k-fold cross validation for hyperparameter tuning, feature selection via feature importance identification were integrated within the models.ResultsA total of 5 classifiers were developed, with two built using 35 cytokine predictive features and three built using a subset of cytokines, selected by variable importance techniques namely Random Forest, ReliefF and Boruta. The best Area under Receiver Operating Characteristic (AUROC) based accuracy of .99 was achieved by the Random Forest classifier with 35 cytokine biomarkers. The second-best AUROC accuracy was achieved by the k-NN model using cytokines selected by the Random Forest variable importance selection mechanism.ConclusionsPresently, as large-scale efforts are gaining momentum to enable early, fast, reliable, affordable, and accessible detection of individuals at risk for CAD, the application of powerful ML algorithms can be leveraged as a supplement to the conventional treatments such as angiography. The early detection can be further improved by incorporating 65 novel and sensitive cytokines biomarkers. Investigation of the emerging role of cytokines in CAD can materially enhance the detection of risk and the discovery of mechanisms of disease that can lead to new therapeutic modalities.
Comparison of Nearest Neighbor and Caliper Algorithms in Outcome Propensity Score Matching to Study the Relationship between Type 2 Diabetes and Coronary Artery Disease