Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, RF, SVM, ADA and MU—Averaged over all the 864 [O,P,T] window settings: Averaged for 3,683 patients in the online mode and over 5-fold cross validation in the offline mode.

<p>Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, RF, SVM, ADA and MU—Averaged over all the 864 [O,P,T] window settings: Averaged for 3,683 patients in the online mode and over 5-fold cross validation in the offline mode.</p

[O,P,T] parameters for a predictive algorithm.

<p>Note that the Test and Observation windows can vary in duration independently of each other.</p

Accuracy, sensitivity and specificity of our algorithm (DBC), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), AdaBoost (ADA) and Mean based method (MU) at prediction window of 120 minutes, averaged over 144 choices of [O,T] settings and over (LEFT:) 3683 patients (online)/ (RIGHT:) 5–fold cross validation (offline).

<p>Accuracy, sensitivity and specificity of our algorithm (DBC), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), AdaBoost (ADA) and Mean based method (MU) at prediction window of 120 minutes, averaged over 144 choices of [O,T] settings and over (LEFT:) 3683 patients (online)/ (RIGHT:) 5–fold cross validation (offline).</p

Mean performance of algorithm DBC in the three regions learned—Averaged over all the 864 [O,P,T] window settings: Over 5-fold cross validation in the offline mode.

<p>Mean performance of algorithm DBC in the three regions learned—Averaged over all the 864 [O,P,T] window settings: Over 5-fold cross validation in the offline mode.</p

Schematic of our novel classifier.

<p>The feature space is divided into three regions based on the values of <i>μ</i>₁, <i>μ</i>₂, <i>σ</i>₁, <i>σ</i>₂, <i>κ</i>₀. A test sample in the AHE region is classified into class A, Non-AHE region is classified into class NA. A distance-based approach is used for test samples in the Uncertainty region. The arrows for a point in the uncertainty region (shown only for one of the points), show that distances are calculated with respect points outside the uncertainty region only.</p

Model performance based on concordance index.

<p>Model performance based on concordance index.</p

Effect size estimates, fraction of patients with events (F) and number of Samples (N) for most important clinical variables (n = 789).

<p>Effect size estimates, fraction of patients with events (F) and number of Samples (N) for most important clinical variables (n = 789).</p

Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, Linear SVM—Averaged over all the 864 [O,P,T] window settings: Averaged for 3,683 patients in the online mode and over 5-fold cross validation in the offline mode.

<p>Mean (standard deviation in parentheses) of performance of algorithms—Our (DBC), KNN, Linear SVM—Averaged over all the 864 [O,P,T] window settings: Averaged for 3,683 patients in the online mode and over 5-fold cross validation in the offline mode.</p

Sample characteristics of the HFHS heart failure study cohort.

<p>Sample characteristics of the HFHS heart failure study cohort.</p

Comparison of empirical power and Type-1 error rates of gene-based association tests for simulated datasets assuming linkage equilibrium.

<p>DSL denotes the number of disease susceptibility markers. Machine learning test is based on ensemble learning variation 1 with the following components: logistic regression, support vector machine with linear kernel and random forests with m_try = 1 and n_tree = 1000.</p><p>Comparison of empirical power and Type-1 error rates of gene-based association tests for simulated datasets assuming linkage equilibrium.</p