Cervical cancer is one of the most dangerous diseases that affect women worldwide. The diagnosis of cervical cancer is challenging, costly, and time-consuming. Existing literature has focused on traditional machine learning techniques and deep learning to identify and predict cervical cancer. This research proposes an integrated system of Genetic Algorithm (GA), Multilayer Perceptron (MLP), and Principal Component Analysis (PCA) that accurately predicts cervical cancer. GA is used to optimize the MLP hyperparameters, and the MLPs act as simulators within the GA to provide the prediction accuracy of the solutions. The proposed method uses PCA to transform the available factors; the transformed features are subsequently used as inputs to the MLP for model training. To contrast with the PCA method, different subsets of the original factors are selected. The performance of the integrated system of PCA–GA–MLP is compared with nine different classification algorithms. The results indicate that the proposed method outperforms the studied classification algorithms. The PCA–GA–MLP model achieves the best accuracy in diagnosing Hinselmann, Biopsy, and Cytology when compared to existing approaches in the literature that were implemented on the same dataset. This study introduces a robust tool that allows medical teams to predict cervical cancer in its early stage.

Pressure Injuries (PI) are one of the most common health conditions in the United States. Most acute or long-term care patients are at risk of developing PI. Machine Learning (ML) has been utilized to manage patients with PI, in which one systematic review describes how ML is used in PI management in 32 studies. This research, different from the previous systematic review, summarizes the previous contributions of ML in PI from January 2007 to July 2022, categorizes the studies according to medical specialties, analyzes gaps, and identifies opportunities for future research directions. PRISMA guidelines were adopted using the four most common databases (PubMed, Web of Science, Scopus, and Science Direct) and other resources, which result in 90 eligible studies. The reviewed articles are divided into three categories based on PI time of occurrence: before occurrence (48%); at time of occurrence (16%); and after occurrence (36%). Each category is further broken down into sub-fields based on medical specialties, which result in sixteen specialties. Each specialty is analyzed in terms of methods, inputs, and outputs. The most relevant and potentially useful applications and methods in PI management are outlined and discussed. This includes deep learning techniques and hybrid models, integration of existing risk assessment tools with ML that leads to a partnership between provider assessment and patients’ Electronic Health Records (EHR).

Background: Large language models (LLMs) have fundamentally transformed approaches to natural language processing tasks across diverse domains. In health care, accurate and cost-efficient text classification is crucial—whether for clinical note analysis, diagnosis coding, or other related tasks—and LLMs present promising potential. Text classification has long faced multiple challenges, including the need for manual annotation during training, the handling of imbalanced data, and the development of scalable approaches. In health care, additional challenges arise, particularly the critical need to preserve patient data privacy and the complexity of medical terminology. Numerous studies have leveraged LLMs for automated health care text classification and compared their performance with traditional machine learning–based methods, which typically require embedding, annotation, and training. However, existing systematic reviews of LLMs either do not specialize in text classification or do not focus specifically on the health care domain. Objective: This research synthesizes and critically evaluates the current evidence in the literature on the use of LLMs for text classification in health care settings. Methods: Major databases (eg, Google Scholar, Scopus, PubMed, ScienceDirect) and other resources were queried for papers published between 2018 and 2024, following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, resulting in 65 eligible research articles. These studies were categorized by text classification type (eg, binary classification, multilabel classification), application (eg, clinical decision support, public health and opinion analysis), methodology, type of health care text, and the metrics used for evaluation and validation. Results: The systematic review includes 65 research articles published between 2020 and Q3 2024, showing a significant increase in publications over time, with 28 papers published in Q1-Q3 2024 alone. Fine-tuning was the most common LLM-based approach (35 papers), followed by prompt engineering (17 papers). BERT (Bidirectional Encoder Representations from Transformers) variants were predominantly used for multilabel classification (50%), whereas closed-source LLMs were most commonly applied to binary (44.0%) and multiclass (30.6%) classification tasks. Clinical decision support was the most frequent application (29 papers). Over 80% of studies used English-language datasets, with clinical notes being the most common text type. All studies employed accuracy-related metrics for evaluation, and the findings consistently showed that LLMs outperformed traditional machine learning approaches in health care text classification tasks. Conclusions: This review identifies existing gaps in the literature and highlights future research directions for further investigation.