METHODS: This paper presents a new machine learning approach that uses MICE for mitigating missing data, the IQR for handling outliers and SMOTE to address first imbalance distance. Additionally, to select optimal features, we introduce the Hybrid 2-Tier Grasshopper Optimization with L2 regularization methodology which we call GOL2-2T. One of the promising methods to improve the predictive modelling is an Adaboost decision fusion (ABDF) ensemble learning algorithm with babysitting technique implemented for the hyperparameters tuning. The accuracy, recall, and AUC score will be considered as the measures for assessing the model.
RESULTS: On the results, our heart disease prediction model yielded an accuracy of 83.0%, and a balanced F1 score of 84.0%. The integration of SMOTE, IQR outlier detection, MICE, and GOL2-2T feature selection enhances robustness while improving the predictive performance. ABDF removed the impurities in the model and elaborated its effectiveness, which proved to be high on predicting the heart disease.
DISCUSSION: These findings demonstrate the effectiveness of additional machine learning methodologies in medical diagnostics, including early recognition improvements and trustworthy tools for clinicians. But yes, the model's use and extent of work depends on the dataset used for it really. Further work is needed to replicate the model across different datasets and samples: as for most models, it will be important to see if the results are generalizable to populations that are not representative of the patient population that was used for the current study.
METHODS: Guided by the PRISMA framework, we conducted a rigorous search through the PubMed, Web of Science and Scopus databases, analyzing 254 articles. Each article was scrutinized against pre-defined inclusion criteria, yielding a refined selection of 14 studies worthy of in-depth analysis.
RESULTS: The trends in using morphological approaches were identified for analyzing osteoblast and osteoclast differentiation. The three most used techniques for osteoblasts were Alizarin Red S (mineralization; six articles), von Kossa (mineralization; three articles) and alkaline phosphatase (ALP; two articles) followed by one article on Giemsa staining (cell morphology) and finally immunochemistry (three articles involved Vinculin, F-actin and Col1 biomarkers). For osteoclasts, tartrate-resistant acid phosphatase (TRAP staining) has the highest number of articles (six articles), followed by two articles on DAPI staining (cell morphology), and immunochemistry (two articles with VNR, Cathepsin K and TROP2. The study involved four stem cell types: peripheral blood monocyte, mesenchymal, dental pulp, and periodontal ligament.
CONCLUSION: This review offers a valuable resource for researchers, with Alizarin Red S and TRAP staining being the most utilized morphological procedures for osteoblasts and osteoclasts, respectively. This understanding provides a foundation for future research in this rapidly changing field.
METHODS: Forty participants were randomly assigned to four groups: control group (CG, n = 10), compound set training group (CSG, n = 10), pyramid set training group (PSG, n = 10), and superset training group (SSG, n = 10). Excluding the CG, the other three groups underwent an 8-week resistance training program, three sessions per week, at 60%-80% of 1RM intensity for 60-90 min per session. Assessments included body composition, physical fitness components, 1RM, isokinetic muscle functions, and biomechanical properties (muscle frequency, stiffness, etc.) of the rectus abdominis and external oblique muscles.
RESULTS: The PSG demonstrated the most significant improvement in relative peak torque during isokinetic testing of the shoulder and knee joints. Compared to the CG, all exercise groups exhibited positive effects on back strength, sprint performance, 1RM, and core muscle biomechanics. Notably, the PSG showed superior enhancement in external oblique stiffness. However, no significant differences were observed among the exercise groups for rectus abdominis biomechanical properties.
DISCUSSION: Structured resistance training effectively improved maximal strength, functional performance, and core muscle biomechanics. The pyramidal training modality conferred specific benefits for isokinetic muscle functions and external oblique stiffness, suggesting its efficacy in enhancing force production capabilities and core stability.
OBJECTIVE: To summarise the protocol and statistical analysis plan for the Mega-ROX HIE trial.
DESIGN SETTING AND PARTICIPANTS: Mega-ROX HIE is an international randomised clinical trial that will be conducted within an overarching 40,000-participant registry-embedded clinical trial comparing conservative and liberal ICU oxygen therapy regimens. We expect to enrol approximately 4000 participants with suspected HIE following a cardiac arrest who are receiving invasive mechanical ventilation in the ICU.
MAIN OUTCOME MEASURES: The primary outcome is in-hospital all-cause mortality up to 90 days from the date of randomisation. Secondary outcomes include duration of survival, duration of mechanical ventilation, ICU length of stay, hospital length of stay, and the proportion of participants discharged home.
RESULTS AND CONCLUSIONS: Mega-ROX HIE will compare the effect of conservative vs. liberal oxygen therapy regimens on day-90 in-hospital mortality in adults in the ICU with suspected HIE following a cardiac arrest. The protocol and planned analyses are reported here to mitigate analysis bias.
TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry (ACTRN 12620000391976).
METHODS: To predict CD while prioritizing patient privacy, our study employed data anonymization involved adding Laplace noise to sensitive features like age and gender. The anonymized dataset underwent analysis using a differential privacy (DP) framework to preserve data privacy. DP ensured confidentiality while extracting insights. Compared with Logistic Regression (LR), Gaussian Naïve Bayes (GNB), and Random Forest (RF), the methodology integrated feature selection, statistical analysis, and SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for interpretability. This approach facilitates transparent and interpretable AI decision-making, aligning with responsible AI development principles. Overall, it combines privacy preservation, interpretability, and ethical considerations for accurate CD predictions.
RESULTS: Our investigations from the DP framework with LR were promising, with an area under curve (AUC) of 0.848 ± 0.03, an accuracy of 0.797 ± 0.02, precision at 0.789 ± 0.02, recall at 0.797 ± 0.02, and an F1 score of 0.787 ± 0.02, with a comparable performance with the non-privacy framework. The SHAP and LIME based results support clinical findings, show a commitment to transparent and interpretable AI decision-making, and aligns with the principles of responsible AI development.
CONCLUSIONS: Our study endorses a novel approach in predicting CD, amalgamating data anonymization, privacy-preserving methods, interpretability tools SHAP, LIME, and ethical considerations. This responsible AI framework ensures accurate predictions, privacy preservation, and user trust, underscoring the significance of comprehensive and transparent ML models in healthcare. Therefore, this research empowers the ability to forecast CD, providing a vital lifeline to millions of CD patients globally and potentially preventing numerous fatalities.