Perspective Chapter: Optimized Random Forests for Effective and Efficient Healthcare Data Analytics

The relentless growth of data, accelerated since the industrial revolution and now amplified by the digital age, presents unprecedented opportunities and challenges for the healthcare industry. As the global datasphere is projected to expand from 33 zettabytes to 175 zettabytes between 2018 and 2025, leveraging this data through advanced machine learning (ML) algorithms has become crucial, especially in the wake of the COVID-19 pandemic. This study explores the integration of ML and data analytics in healthcare, demonstrating their potential to revolutionize patient care, disease diagnosis, treatment personalization, administrative efficiency, and drug development. By utilizing various ML algorithms, including Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree, and Random Forest, on a diverse dataset, this research evaluates their performance in predicting health outcomes and improving healthcare services. Random Forest and KNN algorithms showed superior performance in accuracy and reliability, highlighting the importance of selecting appropriate models based on dataset characteristics. The findings underscore the transformative potential of ML and data analytics in healthcare, emphasizing the need for robust datasets, ethical considerations, and data security to maximize their benefits. This integration promises to enhance proactive care, optimize resource allocation, and personalize medical treatments, ultimately leading to improved patient outcomes and operational efficiencies.

This study targets service-delivery improvements in South African public hospitals by pairing Big Data analytics with machine learning, implemented under a quantitative Cross-Industry Standard Process for Data Mining (CRISP-DM) workflow. We (i) identified determinants of effective Big Data analytics in public healthcare, (ii) compared analytical methods, (iii) built a predictive model of service outcomes, and (iv) assessed its performance and interpretability. Using a synthetic hospital dataset, we engineered features and trained a stacked ensemble (XGBoost, Random Forest, CatBoost, LightGBM) to classify outcomes as Improved, Unchanged, or Worsened. Baseline models (e.g., Support Vector Machine (SVM), logistic regression) were explored but not retained due to scalability and multiclass performance constraints. The final ensemble achieved 84% accuracy with class-wise Area Under the Curves (AUCs) of 0.93-0.95. Post-hoc SHapley Additive exPlanations (SHAP) analysis highlighted Satisfaction Rating, Age, and log (Wait Time) as the most influential features, and revealed interaction effects such as Wait-length interactions that align with operational intuition. To support practical use, we prototyped a Streamlit application that delivers near-real-time, explainable predictions for front-line decision support. The findings indicate that Machine Learning (ML)-driven Big Data analytics can provide scalable, interpretable insights to inform predictive decision-making, resource allocation, and patient-flow management in the public sector. While results are demonstrated on synthetic data, the approach establishes a deployable pathway for hospital analytics in South Africa and a foundation for validation on real-world datasets.

Healthcare sector is one of the prominent sectors in which a lot of data can be collected not only in terms of health but also in terms of finances. Major frauds happen in the healthcare sector due to the utilization of credit cards as the continuous enhancement of electronic payments, and credit card fraud monitoring has been a challenge in terms of financial condition to the different service providers. Hence, continuous enhancement is necessary for the system for detecting frauds. Various fraud scenarios happen continuously, which has a massive impact on financial losses. Many technologies such as phishing or virus-like Trojans are mostly used to collect sensitive information about credit cards and their owner details. Therefore, efficient technology should be there for identifying the different types of fraudulent conduct in credit cards. In this paper, various machine learning and deep learning approaches are used for detecting frauds in credit cards and different algorithms such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network are skewed for training the other standard and abnormal features of transactions for detecting the frauds in credit cards. For evaluating the accuracy of the model, publicly available data are used. The different algorithm results visualized the accuracy as 96.1%, 94.8%, 95.89%, 97.58%, and 92.3%, corresponding to various methodologies such as Naive Bayes, Logistic Regression, K-Nearest Neighbor (KNN), Random Forest, and the Sequential Convolutional Neural Network, respectively. The comparative analysis visualized that the KNN algorithm generates better results than other approaches.

Machine learning applications for anomaly detection in Smart Water Metering Networks: A systematic review

This paper presents a systematic literature review with respect to application of data science and machine learning (ML) to heart failure (HF) datasets with the intention of generating both a synthesis of relevant findings and a critical evaluation of approaches, applicability and accuracy in order to inform future work within this field. This paper has a particular intention to consider ways in which the low uptake of ML techniques within clinical practice could be resolved. Literature searches were performed on Scopus (2014-2021), ProQuest and Ovid MEDLINE databases (2014-2021). Search terms included ‘heart failure’ or ‘cardiomyopathy’ and ‘machine learning’, ‘data analytics’, ‘data mining’ or ‘data science’. 81 out of 1688 articles were included in the review. The majority of studies were retrospective cohort studies. The median size of the patient cohort across all studies was 1944 (min 46, max 93260). The largest patient samples were used in readmission prediction models with the median sample size of 5676 (min. 380, max. 93260). Machine learning methods focused on common HF problems: detection of HF from available dataset, prediction of hospital readmission following index hospitalization, mortality prediction, classification and clustering of HF cohorts into subgroups with distinctive features and response to HF treatment. The most common ML methods used were logistic regression, decision trees, random forest and support vector machines. Information on validation of models was scarce. Based on the authors’ affiliations, there was a median 3:1 ratio between IT specialists and clinicians. Over half of studies were co-authored by a collaboration of medical and IT specialists. Approximately 25% of papers were authored solely by IT specialists who did not seek clinical input in data interpretation. The application of ML to datasets, in particular clustering methods, enabled the development of classification models assisting in testing the outcomes of patients with HF. There is, however, a tendency to over-claim the potential usefulness of ML models for clinical practice. The next body of work that is required for this research discipline is the design of randomised controlled trials (RCTs) with the use of ML in an intervention arm in order to prospectively validate these algorithms for real-world clinical utility.

WITHDRAWN: Evaluation, prediction and implementation patterns of network traffic malware using machine learning

BackgroundDespite the interest in machine learning (ML) algorithms for analyzing real-world data (RWD) in healthcare, the use of ML in predicting time-to-event data, a common scenario in clinical practice, is less explored. ML models are capable of algorithmically learning from large, complex datasets and can offer advantages in predicting time-to-event data. We reviewed the recent applications of ML for survival analysis using RWD in healthcare.MethodsPUBMED and EMBASE were searched from database inception through March 2023 to identify peer-reviewed English-language studies of ML models for predicting time-to-event outcomes using the RWD. Two reviewers extracted information on the data source, patient population, survival outcome, ML algorithms, and the Area Under the Curve (AUC).ResultsOf 257 citations, 28 publications were included. Random survival forests (N = 16, 57%) and neural networks (N = 11, 39%) were the most popular ML algorithms. There was variability across AUC for these ML models (median 0.789, range 0.6–0.950). ML algorithms were predominately considered for predicting overall survival in oncology (N = 12, 43%). ML survival models were often used to predict disease prognosis or clinical events (N = 27, 96%) in the oncology, while less were used for treatment outcomes (N = 1, 4%).ConclusionsThe ML algorithms, random survival forests and neural networks, are mainly used for RWD to predict survival outcomes such as disease prognosis or clinical events in the oncology. This review shows that more opportunities remain to apply these ML algorithms to inform treatment decision-making in clinical practice. More methodological work is also needed to ensure the utility and applicability of ML models in survival outcomes.

The objective of this document is to identify the most accurate machine learning (ML) models for predicting the value of a real estate property, based on a systematic literature review (SLR). It was conducted on research published between 2022 and 2023 that analyzed the accuracy of ML models in real estate valuation. Information was extracted regarding the ML models used, the databases employed, and the models highlighted for their accuracy. A variety of ML models used in real estate valuation were identified, including Random Forest (RF), XGBoost, Gradient Boosting Machine (GBM), Linear Regression (LR), and Lasso Regression. RF and LR models stood out as the most accurate in the analyzed research, showing that the accuracy of ML models varies depending on the database, property characteristics, and valuation context. It was concluded that ML models like RF and LR are promising tools for improving the accuracy of real estate valuation. Additionally, the choice of the best ML model depends on factors such as the database, property characteristics, and specific valuation objectives. Further research is needed on the application of ML in real estate valuation, considering aspects such as model interpretability and the real estate market context.

Intraoperative complications during cataract surgery, while uncommon, can significantly impact visual outcomes and identifying high-risk patients preoperatively improves surgical planning and supports personalized care. This study presents an early application of advanced machine learning (ML) models for predicting multiple intraoperative complications using routine preoperative data. We analyzed 3,957 cataract surgeries performed at a tertiary hospital and developed four ML models, XGBoost, Random Forest, CatBoost and TabPFN, to classify cases into three categories: no complications, posterior capsule rupture/vitreous loss and other complications. To address class imbalance due to the predominance of uncomplicated cases, random undersampling was applied during training. Model performance was assessed using area under the curve (AUC) and F1-score. SHAP (SHapley Additive exPlanations) analysis was used to interpret feature importance. TabPFN achieved the highest AUC score (0.62), followed closely by Random Forest and CatBoost (0.61 each), with Random Forest also demonstrating the highest F1-score (0.46). SHAP analysis highlighted age, tamsulosin use and surgeon experience as key predictors. This study provides a proof of concept for the feasibility of using machine learning for preoperative risk modeling of intraoperative complications in cataract surgery. While predictive performance was modest, validation on larger or external datasets is encouraged to further assess its potential role in surgical planning.

Ensemble machine learning (ML) demonstrated potential for improving predictions based on big health care data. We developed and validated interpretable ensemble ML models in evaluating the nonadherence and nonpersistence of biological or targeted synthetic disease-modifying antirheumatic drugs (b/tsDMARDs) in rheumatoid arthritis (RA). This retrospective study used 5% Medicare claims data including older (aged ≥65 years) patients initiating b/tsDMARDs (the index date) between 2013 and 2019, who were diagnosed with RA (the International Classification of Diseases, Ninth and Tenth revision codes). Nonadherence, defined as medication possession ratio <80%, was evaluated during the 12-month follow-up. Nonpersistence was defined as duration (in days) from the index date until treatment gap ≥60 days during the follow-up period, with death and insurance disenrollment as censoring criteria. Data were split into 75% for model training and 25% for evaluation, with patients' demographics and clinical characteristics measured in the baseline period. Machine learning ensemble classification models (random forest, eXtreme Gradient Boosting [XGBoost], and prediction rule ensembles [PRE]) evaluated the binary nonadherence outcome and ML ensemble survival models (random survival forest, XGBoost survival, and PRE survival) assessed time until nonpersistence. Using the testing cohort, concordance index (C-index) was reported for the ML survival models and AUC was reported for ML classification models. We identified 3927 eligible patients with RA (mean age 73 ± 6 years; 75% female). About 53.65% of them were adherent (medication possession ratio 0.72 [0.31]; 0.01-1) and 18.5% of them had the risk of nonpersistence (mean time to nonpersistence 1110 ± 670 days) in the 12-month follow-up. Compared with PRE (AUC, 0.6271; 95% CI, 0.5923-0.6618), random forest (0.6315; 95% CI, 0.5969-0.6661; P = 0.7127) and XGBoost (0.6277; 95% CI, 0.5930-0.6624; P = 0.9643) had comparable performance in predicting nonadherence. Age, claims-based index for RA severity, type of b/tsDMARDs, frailty score, cancer and Elixhauser comorbidity index were ranked commonly as the top features for nonadherence. With reference to PRE survival (C-index, 0.634; 95% CI, 0.611-0.658), random survival forest (C-index, 0.661; 95% CI, 0.665-0.675; P = 0.0276) and XGBoost Survival (C-index, 0.670; 95% CI, 0.666-0.676; P = 0.0033) had improved performance for predicting nonpersistence. Age, Elixhauser score, frailty score, and claims-based index for RA severity were commonly found as the top predictors for nonpersistence. We developed ML models predicting nonadherence and nonpersistence of b/tsDMARDs for older adults with RA. There is potential to leverage ML for understanding patients' behavior of utilizing b/tsDMARDs treatment with a goal towards personalized medicine in RA.

Mastitis has detrimental effects on the world's dairy industry, reducing animal health, milk production and quality, as well as income for farmers. In addition, consumers' growing interest in food safety and rational usage of antibiotics highlights the need to develop novel strategies to improve mastitis detection, prevention, and management. In the present study we applied machine learning (ML) analyses to predict presence or absence of subclinical mastitis in Italian Mediterranean buffaloes, exploiting information collected the previous month during routine milk recording procedures, as well as climatic data. The data set included 3,891 records of 1,038 buffaloes from 6 herds located in Basilicata Region (South Italy). Prediction models were developed using 4 different ML algorithms (Generalized Linear Model, Support Vector Machines, Random Forest, and Neural Network) and 2 data set splitting approaches for the creation of the training and test sets (by record or by animal ID number, always with 80% of the data used for model training and the remaining 20% for model testing). Support Vector Machine was the best method to predict high or low somatic cell count at the subsequent test-day record in the validation set, and therefore it was used to estimate the contribution of each feature to the best model. Independently from the data set splitting approach, the most important features were somatic cell score, differential somatic cell count, electrical conductivity, and milk production. Among climatic data, the most informative were temperature and relative humidity. When the data were split by animal ID, an improvement in models' predictive performance on the test set was observed, suggesting this as the most appropriate data splitting approach in data sets with repeated measures to avoid data leakage. According to different metrics, Neural Network was the best method for making predictions on the test set. Our findings confirmed the promising role of ML methods to improve prevention and surveillance of subclinical mastitis, exploiting the large amount of data currently available to identify animals that would possibly have high somatic cell count the subsequent month.

Machine-learning insights into nitrate-reducing communities in a full-scale municipal wastewater treatment plant

Purpose - This paper reviews 45 Scopus-indexed articles, a significant body of research, to identify the degree, scope and purposes of machine learning (ML) adoption in the core functions of talent acquisition (TA). Design/methodology/approach—This review has employed a semi-systematic approach, a unique, comprehensive literature analysis method involving a structured search and review process. This approach, emerging from multiple disciplines and using different methods and theoretical frameworks, was deemed appropriate due to ML research's diverse origins and methods, ensuring a thorough review. Findings: The review suggests that talent acquisition has embraced ML and is attracting attention from technology-oriented practitioners. ML applications, particularly those using random forest and decision tree algorithms for classification, are most robust in recruitment and talent acquisition. However, the early stage of ML applications for complex tasks underscores the need for collaboration between HR experts and ML specialists, highlighting the field's interdisciplinary nature. (Fang et al., 2016). Originality/value: This review significantly enhances the understanding of ML integration in talent acquisition in the current digitalisation era. More importantly, it highlights the potential of ML applications to significantly improve the efficiency and effectiveness of talent acquisition functions, thereby enhancing employee performance and contributing to organisational success. This potential should inspire hope and excitement about the future of talent acquisition.

The healthcare industry generates massive amounts of data, from patient records to clinical trial results, which can be utilized to improve patient outcomes, reduce costs, and enhance the efficiency of healthcare delivery. Big data analytics is a technology that can help healthcare organizations make sense of these vast amounts of data. The potential benefits of big data analytics in healthcare include improved patient outcomes, reduced costs, enhanced efficiency, and more accurate diagnoses. However, big data analytics also presents significant challenges, including data privacy and security, and the complexity of healthcare data. This research paper aims to explore the role of big data analytics in healthcare, focusing on its potential benefits, challenges, and applications. By understanding the potential of big data analytics in healthcare and the challenges it presents, healthcare organizations can leverage this technology to improve healthcare delivery and patient outcomes.

Data analytics has emerged as a transformative force in optimizing hospital resource allocation and decision-making processes. The integration of advanced technologies such as artificial intelligence, machine learning, and the Internet of Things has significantly enhanced the efficiency and effectiveness of healthcare delivery systems. These technologies enable hospitals to leverage vast amounts of data for improved clinical support, resource allocation, and operational efficiency. The implementation of data analytics in healthcare has led to several key benefits, including improved patient outcomes, cost reduction, enhanced predictive analysis capabilities, and more efficient resource optimization. However, the adoption of data analytics in healthcare settings faces several challenges, such as issues related to data quality and standardization, privacy and security concerns, and resistance to change within organizational structures. Addressing these challenges requires a comprehensive approach involving technological advancements, policy reforms, and cultural shifts within healthcare institutions. Emerging trends in healthcare data analytics point towards increased integration of artificial intelligence and deep learning technologies, promising to further enhance predictive modeling capabilities, real-time analytics, and the incorporation of diverse data sources for more precise and efficient healthcare delivery. While data analytics offers immense potential for optimizing hospital resource allocation and decision-making, its successful implementation necessitates ongoing research, interdisciplinary collaboration, and the development of robust frameworks to address ethical and practical challenges.