Supervised classification of Preeclampsia clinical cases using datasets from MALDI-TOF-MS and machine learning tools

Background Preeclampsia is a pregnancy-induced disorder characterized by hypertension and high levels of proteinuria after 20 weeks of pregnancy. This condition significantly raises the risk of maternal-fetal death and increases the risk of vascular diseases after pregnancy. However, timely and unequivocal diagnosis in the first weeks allows access to appropriate medical follow-up. In this work, we performed a supervised classificatory analysis by machine learning using the protein profiles present in blood serum samples from 97 controls and 67 patient cases obtained by matrix-assisted laser ionization/desorption time-of-flight mass spectrometry. Results The protein profile was obtained from peptide mass fingerprinting of the samples by the Filter-Assisted Sample Preparation (FASP) protocol and subsequent acquisition of the mass spectra. The spectrum data analysis using machine learning algorithms demonstrated high performance in classifying cases and controls, with an overall accuracy of 88% and a sensitivity of 0.90 and 0.85 for predicting positive cases and negative controls, respectively. Conclusions These results highlight the reliability and versatility of analyzing and processing spectral data from mass spectrometry using artificial intelligence tools to study of preeclampsia. This study could pave the way for future applications in clinical diagnostics, offering new alternatives for improved patient outcomes.