AI-enhanced non-invasive diagnosis of chronic kidney disease using LIBS of fingernail biomarkers.

Chronic kidney disease (CKD) is a systemic condition that leads to progressive renal failure and metabolic imbalances that may be detected in the keratinized bio-tissues of the body such as fingernails. Nevertheless, still its early detection is difficult due to the invasive nature of current clinical screening approaches. This research paper provides a precise, non-invasive, and AI-enhanced diagnostic model of CKD screening through the combination of Laser-Induced Breakdown Spectroscopy (LIBS) and the cutting-edge ensemble machine learning to determine the elemental patterns of fingernail biomarkers. In this study, 55 participants with chronic kidney disease and 45 healthy controls were taken as the subjects and their emission spectra were measured with a nanosecond LIBS spectrometer using a laser of a fundamental wavelength @ 1064 nm. To handle the high-dimensional spectral data, Principal Component Analysis (PCA) was used as a feature extraction approach, forming the basis for novel machine-learning models. A sophisticated ensemble learning algorithm specifically Extreme Gradient Boosting (XGBoost) was applied to examine the biochemical change between CKD and control samples and the predictive quality was tested by a relative comparison with a Support Vector Machine (SVM) algorithm. Using 10-fold cross-validation, the XGBoost model outperformed SVM, attaining 97% accuracy, 98% sensitivity, 96% precision and specificity, and a 97% F1-Score. Moreover, external validation on a separate dataset showed that the model is robust and generalizable with 95% accuracy, 97% sensitivity, and an F1-score of 95%. The results conclude that fingernail-based LIBS integrated with ensemble machine learning is a potential and non-invasive instrument for CKD classification.