Polycystic Ovary Syndrome (PCOS) is a hormonal disorder primarily affecting women of reproductive age, characterized by irregular menstrual cycles, elevated male hormones, and ovarian cysts. Early detection and treatment are crucial to prevent long-term complications. This research utilizes clinical data from Kaggle to develop a non-invasive PCOS diagnostic system. The authors conducted comprehensive data preprocessing, feature engineering, and exploratory data analysis (EDA). The refined dataset was incorporated into various default machine learning (ML) algorithms, including LR, LDA, GNB, SVM, XGB, DT, AB, RF, and KNN, for PCOS classification with varying train test ratios 70:30 to 80:20. To further enhance the model’s performance, the authors hybridized all the ML models with Particle Swarm Optimization (PSO). Remarkably, the proposed LR+PSO model achieved the highest accuracy at 96.30%, demonstrating exceptional proficiency with an 80:20 train-test ratio. It significantly improved sensitivity to 94.44%, indicating enhanced detection of positive cases, all while maintaining the highest specificity at 97.22% and precision at 94.44% compared to other models. These results highlight a substantial improvement in integrated models, emphasizing the potential of this novel approach to enhance PCOS diagnosis in terms of accuracy and efficiency, ultimately benefiting individuals with PCOS in their treatment journey.

Arrhythmia, a common cardiovascular disorder, refers to the abnormal electrical activity within the heart, leading to irregular heart rhythms. This condition affects millions of people worldwide, with severe implications on cardiac function and overall health. Arrhythmias can strike anyone at any age which is a significant cause of morbidity and mortality on a global scale. About 80% of deaths related to heart disease are caused by ventricular arrhythmias. This research investigated the application of an optimized multi-objectives supervised Machine Learning (ML) models for early arrhythmia diagnosis. The authors evaluated the model’s performance on the arrhythmia dataset from the UCI ML repository with varying train-test splits (70:30, 80:20, and 90:10). Standard preprocessing techniques such as handling missing values, formatting, balancing, and directory analysis were applied along with Pearson correlation for feature selection, all aimed at enhancing model performance. The proposed optimized RF model achieved impressive performance metrics, including accuracy (95.24%), precision (100%), sensitivity (89.47%), and specificity (100%). Furthermore, the study compared the proposed approach to existing models, demonstrating significant improvements across various performance measures.