Peptidomic-Based Prediction Model for Coronary Heart Disease Using a Multilayer Perceptron Neural Network
This work addresses the need for accurate, non-invasive diagnostic tools for coronary heart disease, a leading cause of death, but it is incremental as it applies an existing neural network method to new biomarker data.
The researchers tackled the problem of diagnosing coronary heart disease (CHD) by developing a non-invasive prediction model using a multilayer perceptron neural network trained on urinary peptide biomarkers, achieving high accuracy with metrics like 95.67% precision and an AUC of 0.9748.
Coronary heart disease (CHD) is a leading cause of death worldwide and contributes significantly to annual healthcare expenditures. To develop a non-invasive diagnostic approach, we designed a model based on a multilayer perceptron (MLP) neural network, trained on 50 key urinary peptide biomarkers selected via genetic algorithms. Treatment and control groups, each comprising 345 individuals, were balanced using the Synthetic Minority Over-sampling Technique (SMOTE). The neural network was trained using a stratified validation strategy. Using a network with three hidden layers of 60 neurons each and an output layer of two neurons, the model achieved a precision, sensitivity, and specificity of 95.67 percent, with an F1-score of 0.9565. The area under the ROC curve (AUC) reached 0.9748 for both classes, while the Matthews correlation coefficient (MCC) and Cohen's kappa coefficient were 0.9134 and 0.9131, respectively, demonstrating its reliability in detecting CHD. These results indicate that the model provides a highly accurate and robust non-invasive diagnostic tool for coronary heart disease.