Polycystic ovary syndrome (PCOS) is a medical condition that impacts millions of women worldwide; however, due to a lack of public awareness, as well as the expensive testing involved in the identification of PCOS, 70% of cases go undiagnosed. Therefore, the primary objective of this study is to design an expert machine learning (ML) model for the early diagnosis of PCOS based on initial symptoms and health indicators; two datasets were amalgamated and preprocessed to accomplish this goal, resulting in a new symptomatic dataset with 12 attributes. An ensemble learning (EL) model, with seven base classifiers, and a deep learning (DL) model, as the meta-level classifier, are proposed. The hyperparameters of the EL model were optimized through the nature-inspired walrus optimization (WaO), cuckoo search optimization (CSO), and random search optimization (RSO) algorithms, leading to the WaOEL, CSOEL, and RSOEL models, respectively. The results obtained prove the supremacy of the designed WaOEL model over the other models, with a PCOS prediction accuracy of 92.8% and an area under the receiver operating characteristic curve (AUC) of 0.93; moreover, feature importance analysis, presented with random forest (RF) and Shapley additive values (SHAP) for positive PCOS predictions, highlights crucial clinical insights and the need for early intervention. Our findings suggest that patients with features related to obesity and high cholesterol are more likely to be diagnosed as PCOS positive. Most importantly, it is inferred from this study that early PCOS identification without expensive tests is possible with the proposed WaOEL, which helps clinicians and patients make better informed decisions, identify comorbidities, and reduce the harmful long-term effects of PCOS.

Noninvasive blood glucose (NIBG) measurement technique has been explored for the last three decades to facilitate diabetes management. Photoplethysmogram (PPG) signal may be used to measure the variations in blood glucose concentration. However, the literature reveals that physiological perturbations such as temperature, skin moisture, and sweat lead to less accurate NIBG measurements. The task of minimizing the effect of these perturbations for accurate measurements is an important research area. Therefore, in the present work, galvanic skin response (GSR) and temperature measurements along with PPG were used to measure blood glucose noninvasively. The data extracted from the sensors were used to estimate blood glucose concentration with the help of two machine learning (ML) techniques, i.e., multiple linear regression (MLR) and artificial neural network (ANN). The accuracy of proposed multisensor system was evaluated by pairing and comparing noninvasive measurements with invasively measured readings. The study was performed on 50 nondiabetic subjects with body mass index (BMI) 27.3 ± 3 kg/m². The results revealed that multisensor NIBG measurement system significantly improves mean absolute prediction error and correlation coefficient in comparison to the techniques reported in the literature.