Investigation of polycystic ovary syndrome using machine learning-assisted laser-induced breakdown spectroscopy

Abstract

Polycystic ovary syndrome (PCOS) is a significant hormonal disease that affects females. PCOS leads to complications such as infertility and metabolic issues. Investigation of PCOS is essential to reduce complications and death rate. This research facilitates the rapid detection of PCOS for effective treatment and prevention. Laser-induced breakdown spectroscopy (LIBS) was employed as a less invasive and sample-preparation-free technique for the investigation of PCOS. The elemental composition of thebsample was analyzed by atomic emission spectra. In our research, the elements such as sodium (Na), nitrogen (N), calcium (Ca), and CN-band were identified in PCOS. Machine learning models, including decision tree, neural networks, support vector machine, random forest, naive Bayes, and logistic regression classifiers, were applied to LIBS spectral data for the classification and discrimination between healthy and PCOS samples. Different models have varying training accuracy ranged from 52.9% to 82.1%, and prediction accuracy ranged from 55.0% to 93.3%. The narrow neural network attained the highest prediction accuracy of 93.3%, and the bilayer neural network attained 82.1% of training accuracy. This preliminary research of LIBS assisted with machine learning provides an alternative method to existing analytical techniques for the investigation of PCOS.