Increasing the explainability and success in classification: many-objective classification rule mining based on chaos integrated SPEA2.

Classification rule mining represents a significant field of machine learning, facilitating informed decision-making through the extraction of meaningful rules from complex data. Many classification methods cannot simultaneously optimize both explainability and different performance metrics at the same time. Metaheuristic optimization-based solutions, inspired by natural phenomena, offer a potential paradigm shift in this field, enabling the development of interpretable and scalable classifiers.

Anterior Segment Findings in Women with Polycystic Ovary Syndrome.

Objectives: This study aimed to investigate the anterior segment in women with polycystic ovary syndrome (PCOS) and to compare them with those of healthy reproductive-age female volunteers.

Materials And Methods: The study included 50 right eyes of 50 women with PCOS (group 1) and 50 right eyes of 50 healthy women (group 2). Intraocular pressure, Schirmer's test, tear film break-up time and central corneal thickness were evaluated in all subjects.