Polycystic Ovary Syndrome (PCOS) is a complex disorder predominantly defined by biochemical hyperandrogenism, oligomenorrhea, anovulation, and in some cases, the presence of ovarian microcysts. This endocrinopathy inhibits ovarian follicle development causing symptoms like obesity, acne, infertility, and hirsutism. Artificial Intelligence (AI) has revolutionized healthcare, contributing remarkably to science and engineering domains. Therefore, we have demonstrated an AI approach using heterogeneous Machine Learning (ML) and Deep Learning (DL) classifiers to predict PCOS among fertile patients. We used an Open-source dataset of 541 patients from Kerala, India. Among all the classifiers, the final multi-stack of ML models performed best with accuracy, precision, recall, and F1-score of 98%, 97%, 98%, and 98%. Explainable AI (XAI) techniques make model predictions understandable, interpretable, and trustworthy. Hence, we have utilized XAI techniques such as SHAP (SHapley Additive Values), LIME (Local Interpretable Model Explainer), ELI5, Qlattice, and feature importance with Random Forest for explaining tree-based classifiers. The motivation of this study is to accurately detect PCOS in patients while simultaneously proposing an automated screening architecture with explainable machine learning tools to assist medical professionals in decision-making.

In the current healthcare setup, the use of Machine Learning has been limited as clinicians diagnose and administer treatment manually. Supervised Learning techniques can help solve many prognostic problems and help clinicians in taking decisions pertaining to healthcare. The paper presents selected machine learning techniques that can be applied for medical data, and in particular some supervised learning techniques with their applications on the analysis of neonatal data. The goal of the paper is to review and discuss the methodology, advantages and disadvantages of supervised learning techniques and the use on neonatal data. In addition, this paper also highlights the model evaluation parameters and also suggests the ways to improve the performance of a model designed for neonatal data analysis.

Databases containing lexical properties are of primary importance to psycholinguistic research and speech-language therapy. Several lexical databases for different languages have been developed in the recent past, but Kannada, a language spoken by 50.8 million people, has no comprehensive lexical database yet. To address this, KannadaLex , a Kannada lexical database, is built as a language resource that contains orthographic, phonological, and syllabic information about words that are sourced from newspaper articles from the past decade. Along with these vital statistics such as the phonological neighborhood, syllable complexity summed syllable and bigram syllable frequencies, and lemma and inflectional family information are stored. The database is validated by correlating frequency, a well-established psycholinguistic feature, with other numerical features. The developed lexical database contains 170K words from varied disciplines, complete with psycholinguistic features. This KannadaLex is a comprehensive resource for psycholinguists, speech therapists, and linguistic researchers for analyzing Kannada and other similar languages. Psycholinguists require lexical data for choosing stimuli to conduct experiments that study the factors that enable humans to acquire, use, comprehend, and produce language. Speech and language therapists query these databases for developing the most efficient stimuli for evaluating, diagnosing, and treating communication disorders, and rehabilitation of speech after brain injuries.