Prediction of wear rates of UHMWPE bearing in hip joint prosthesis with support vector model and grey wolf optimization

One of the greatest challenges in joint arthroplasty is to enhance the wear resistance of ultrahigh molecular weight polyethylene (UHMWPE), which is one of the most successful polymers as acetabular bearings for total hip joint prosthesis. In order to improve UHMWPE wear rates, it is necessary to develop efficient methods to predict its wear rates in various conditions and therefore help in improving its wear resistance, mechanical properties, and increasing its life span inside the body. This article presents a support vector machine using a grey wolf optimizer (SVM-GWO) hybrid regression model to predict the wear rates of UHMWPE based on published polyethylene data from pin on disc (PoD) wear experiments typically performed in the field of prosthetic hip implants. The dataset was an aggregate of 29 different PoD UHMWPE datasets collected from Google Scholar and PubMed databases, and it consisted of 129 data points. Shapley additive explanations (SHAP) values were used to interpret the presented model to identify the most important and decisive parameters that affect the wear rates of UHMWPE and, therefore, predict its wear behavior inside the body under different conditions. The results revealed that radiation doses had the highest impact on the model’s prediction, where high values of radiation doses had a negative impact on the model output. The pronounced effect of irradiation doses and surface roughness on the wear rates of polyethylene was clear in the results when average disc surface roughness (Ra) values were below 0.05 μm, and irradiation doses were above 95 kGy produced 0 mg/MC wear rate. The proposed model proved to be a reliable and robust model for the prediction of wear rates and prioritizing factors that most significantly affect its wear rates. The proposed model can help material engineers to further design polyethylene acetabular linings via improving the wear resistance and minimizing the necessity for wear experiments

Gradient Boosting Machine Based on PSO for prediction of Leukemia after a Breast Cancer Diagnosis

The purpose of this study is to develop an accurate risk predictive model for Chronic Myeloid Leukemia (CML) after an early diagnosis of Breast Cancer (BC). Gradient Boosting Machine (GBM) classification algorithm has been applied to the SEER breast cancer dataset for females diagnosed with BC from 2010 to 2016. A practical Swarm optimizer (PSO) was utilized to optimize the GBM algorithm's hyperparameters to find the SEER dataset's best attributes. Nine attributes were carefully selected to study the growth of CML after a lag time of 6 months following BC's diagnosis. The results revealed that the predictive model could classify patients with breast cancer only and patients with breast cancer with Leukemia by an achieved Accuracy, Sensitivity, and Specificity rates of 98.5 %, 99 %, 97.85 %, respectively. To verify the performance of the proposed algorithm, the accuracy of the suggested GBM classifier model was compared with another state-of-the-art model classifiers KNN (k-Nearest Neighbor), SVM (Support Vector Machine), and RF (Random Forest), which are commonly applied algorithms in most of the existing literature. The results also proved the superior ability of the implemented GBM model Classifier in the classification of breast cancer disease and prediction of patients having Leukemia developed after having breast cancer. These results are promising as they show the integral role of the GBM classifier to classify and predict the tumor with high accuracy and efficiency, which will further help in better cancer diagnosis and treatment of the disease

Automated Triage System for Intensive Care Admissions during the COVID-19 Pandemic Using Hybrid XGBoost-AHP Approach

The sudden increase in patients with severe COVID-19 has obliged doctors to make admissions to intensive care units (ICUs) in health care practices where capacity is exceeded by the demand. To help with difficult triage decisions, we proposed an integration system Xtreme Gradient Boosting (XGBoost) classifier and Analytic Hierarchy Process (AHP) to assist health authorities in identifying patients’ priorities to be admitted into ICUs according to the findings of the biological laboratory investigation for patients with COVID-19. The Xtreme Gradient Boosting (XGBoost) classifier was used to decide whether or not they should admit patients into ICUs, before applying them to an AHP for admissions’ priority ranking for ICUs. The 38 commonly used clinical variables were considered and their contributions were determined by the Shapley’s Additive explanations (SHAP) approach. In this research, five types of classifier algorithms were compared: Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), Random Forest (RF), and Artificial Neural Network (ANN), to evaluate the XGBoost performance, while the AHP system compared its results with a committee formed from experienced clinicians. The proposed (XGBoost) classifier achieved a high prediction accuracy as it could discriminate between patients with COVID-19 who need ICU admission and those who do not with accuracy, sensitivity, and specificity rates of 97%, 96%, and 96% respectively, while the AHP system results were close to experienced clinicians’ decisions for determining the priority of patients that need to be admitted to the ICU. Eventually, medical sectors can use the suggested framework to classify patients with COVID-19 who require ICU admission and prioritize them based on integrated AHP methodologies

Reliable Sarcoidosis Detection Using Chest X-rays with EfficientNets and Stain-Normalization Techniques

Sarcoidosis is frequently misdiagnosed as tuberculosis (TB) and consequently mistreated due to inherent limitations in radiological presentations. Clinically, to distinguish sarcoidosis from TB, physicians usually employ biopsy tissue diagnosis and blood tests; this approach is painful for patients, time-consuming, expensive, and relies on techniques prone to human error. This study proposes a computer-aided diagnosis method to address these issues. This method examines seven EfficientNet designs that were fine-tuned and compared for their abilities to categorize X-ray images into three categories: normal, TB-infected, and sarcoidosis-infected. Furthermore, the effects of stain normalization on performance were investigated using Reinhard’s and Macenko’s conventional stain normalization procedures. This procedure aids in improving diagnostic efficiency and accuracy while cutting diagnostic costs. A database of 231 sarcoidosis-infected, 563 TB-infected, and 1010 normal chest X-ray images was created using public databases and information from several national hospitals. The EfficientNet-B4 model attained accuracy, sensitivity, and precision rates of 98.56%, 98.36%, and 98.67%, respectively, when the training X-ray images were normalized by the Reinhard stain approach, and 97.21%, 96.9%, and 97.11%, respectively, when normalized by Macenko’s approach. Results demonstrate that Reinhard stain normalization can improve the performance of EfficientNet -B4 X-ray image classification. The proposed framework for identifying pulmonary sarcoidosis may prove valuable in clinical use

A Hybrid Multi-Objective Optimizer-Based SVM Model for Enhancing Numerical Weather Prediction: A Study for the Seoul Metropolitan Area

Temperature forecasting is an area of ongoing research because of its importance in all life aspects. However, because a variety of climate factors controls the temperature, it is a never-ending challenge. The numerical weather prediction (NWP) model has been frequently used to forecast air temperature. However, because of its deprived grid resolution and lack of parameterizations, it has systematic distortions. In this study, a gray wolf optimizer (GWO) and a support vector machine (SVM) are used to ensure accuracy and stability of the next day forecasting for minimum and maximum air temperatures in Seoul, South Korea, depending on local data assimilation and prediction system (LDAPS; a model of local NWP over Korea). A total of 14 LDAPS models forecast data, the daily maximum and minimum air temperatures of in situ observations, and five auxiliary data were used as input variables. The LDAPS model, the multimodal array (MME), the particle swarm optimizer with support vector machine (SVM-PSO), and the conventional SVM were selected as comparison models in this study to illustrate the advantages of the proposed model. When compared to the particle swarm optimizer and traditional SVM, the Gray Wolf Optimizer produced more accurate results, with the average RMSE value of SVM for T max and T min Forecast prediction reduced by roughly 51 percent when combined with GWO and 31 percent when combined with PSO. In addition, the hybrid model (SVM-GWO) improved the performance of the LDAPS model by lowering the RMSE values for T max Forecast and T min Forecast forecasting from 2.09 to 0.95 and 1.43 to 0.82, respectively. The results show that the proposed hybrid (GWO-SVM) models outperform benchmark models in terms of prediction accuracy and stability and that the suggested model has a lot of application potentials

Development of a hybrid LSTM with chimp optimization algorithm for the pressure ventilator prediction

Abstract The utilization of mechanical ventilation is of utmost importance in the management of individuals afflicted with severe pulmonary conditions. During periods of a pandemic, it becomes imperative to build ventilators that possess the capability to autonomously adapt parameters over the course of treatment. In order to fulfil this requirement, a research investigation was undertaken with the aim of forecasting the magnitude of pressure applied on the patient by the ventilator. The aforementioned forecast was derived from a comprehensive analysis of many variables, including the ventilator's characteristics and the patient's medical state. This analysis was conducted utilizing a sophisticated computational model referred to as Long Short-Term Memory (LSTM). To enhance the predictive accuracy of the LSTM model, the researchers utilized the Chimp Optimization method (ChoA) method. The integration of LSTM and ChoA led to the development of the LSTM-ChoA model, which successfully tackled the issue of hyperparameter selection for the LSTM model. The experimental results revealed that the LSTM-ChoA model exhibited superior performance compared to alternative optimization algorithms, namely whale grey wolf optimizer (GWO), optimization algorithm (WOA), and particle swarm optimization (PSO). Additionally, the LSTM-ChoA model outperformed regression models, including K-nearest neighbor (KNN) Regressor, Random and Forest (RF) Regressor, and Support Vector Machine (SVM) Regressor, in accurately predicting ventilator pressure. The findings indicate that the suggested predictive model, LSTM-ChoA, demonstrates a reduced mean square error (MSE) value. Specifically, when comparing ChoA with GWO, the MSE fell by around 14.8%. Furthermore, when comparing ChoA with PSO and WOA, the MSE decreased by approximately 60%. Additionally, the analysis of variance (ANOVA) findings revealed that the p-value for the LSTM-ChoA model was 0.000, which is less than the predetermined significance level of 0.05. This indicates that the results of the LSTM-ChoA model are statistically significant