Comparison of Machine Learning Methods for the Analysis of Serum Raman Spectra in the Detection of Chronic Heart Failure

The high sensitivity of Raman spectroscopy to the chemical composition of substances at the molecular level makes it a valuable tool for the diagnosis of chronic heart failure (CHF) through analysis of blood serum. Raman spectroscopy offers a label-free, fast detection method, with highly specific and accurate results when combined with machine learning (ML) techniques. However, it is essential to carefully choose the appropriate ML algorithm for analyzing high-dimensional spectral data to achieve reliable and correct results that are primarily based on the true chemical features and differences of the studied samples, specimens or structures as not all algorithms may deliver the high performance. In this study, we compared four approaches: (1) multivariate curve resolution alternating least squares method in combination with logistic regression (MCR-LR), (2) with linear kernel support vector machine (MCR-SVM), (3) projection on the latent structures with discriminant analysis (PLS-DA), and (4) projection on the latent structures with linear kernel support vector machine (PLS-SVM). These methods were applied to 193 Raman spectra from patients with CHF and 78 from control cases. We found that PLS-DA and PLS-SVM demonstrated the best ROC AUCs, with average value of 0.950 (0.91−0.97, 0.95 CI) and 0.99 (0.94−1.00, 0.95 CI), while MCR-LR and MCR-SVM achieved only 0.50 (0.46−0.53, 0.95 CI) and 0.59 (0.54−0.65, 0.95 CI), respectively.