Boosted machine learning model for predicting intradialytic hypotension using serum biomarkers of nutrition

Advancing intrauterine adhesion severity prediction: Integrative machine learning approach with hysteroscopic cold knife system, clinical characteristics and hematological parameters

Intrauterine Adhesion (IUA) constitute a significant determinant impacting female fertility, potentially leading to infertility, miscarriage, menstrual irregularities, and placental complications. The precise assessment of the severity of IUA is pivotal for the customization of personalized treatment plans, aimed at enhancing the success rate of treatments and mitigating reproductive health risks. This study proposes bTLSMA-SVM-FS, a novel feature selection machine learning model that integrates an enhanced slime mould algorithm (SMA), termed TLSMA, with support vector machines (SVM), aiming to develop a predictive model for assessing the severity of IUA. Initially, a series of optimization comparative experiments were conducted on the TLSMA using the CEC 2017 benchmark functions. By comparing it with eleven meta-heuristic algorithms as well as eleven SOTA algorithms, the experimental outcomes corroborated the superior performance of the TLSMA. Subsequently, the developed bTLSMA-SVM-FS model was employed to conduct a thorough analysis of the clinical features of 107 IUA patients from Wenzhou People's Hospital, comprising 61 cases of moderate IUA and 46 cases of severe IUA. The evaluation results of the model demonstrated exceptional performance in predicting the severity of IUA, achieving an accuracy of 86.700 % and a specificity of 87.609 %. Moreover, the model successfully identified critical factors influencing the prediction of IUA severity, including the preoperative Chinese IUA score, production times, thrombin time, preoperative endometrial thickness, and menstruation. The identification of these key factors not only further validated the efficacy of the proposed model but also provided vital scientific evidence for a deeper understanding of the pathogenesis of IUA and the enhancement of targeted treatment strategies.

Predictive modeling of deep vein thrombosis risk in hospitalized patients: A Q-learning enhanced feature selection model

Deep vein thrombosis (DVT) represents a critical health concern due to its potential to lead to pulmonary embolism, a life-threatening complication. Early identification and prediction of DVT are crucial to prevent thromboembolic events and implement timely prophylactic measures in high-risk individuals. This study aims to examine the risk determinants associated with acute lower extremity DVT in hospitalized individuals. Additionally, it introduces an innovative approach by integrating Q-learning augmented colony predation search ant colony optimizer (QL-CPSACO) into the analysis. This algorithm, then combined with support vector machines (SVM), forms a bQL-CPSACO-SVM feature selection model dedicated to crafting a clinical risk prognostication model for DVT. The effectiveness of the proposed algorithm's optimization and the model's accuracy are assessed through experiments utilizing the CEC 2017 benchmark functions and predictive analyses on the DVT dataset. The experimental results reveal that the proposed model achieves an outstanding accuracy of 95.90% in predicting DVT. Key parameters such as D-dimer, normal plasma prothrombin time, prothrombin percentage activity, age, previously documented DVT, leukocyte count, and thrombocyte count demonstrate significant value in the prognostication of DVT. The proposed method provides a basis for risk assessment at the time of patient admission and offers substantial guidance to physicians in making therapeutic decisions.

Artificial multi-verse optimisation for predicting the effect of ideological and political theory course

Enhancing teaching sufficiency is crucial because low teaching efficiency has always been a widespread issue in ideological and political theory course. Evaluating data on the course is obtained from a freshmen class of 2022 using questionnaires. The data is organised and condensed for mining and analysis. Subsequently, an intelligent artificial multi-verse optimizer (AMVO) method s developed to predict the effect of ideological and political theory course. The proposed AMVO approach was tested against various cutting-edge algorithms to demonstrate its effectiveness and stability on the benchmark functions. The experimental results indicated that AMVO ranked first among the 23 test functions. Furthermore, the binary AMVO enhanced k-nearest neighbour classifier had excellent performance in the art ideological and political theory course in terms of error rate, accuracy, specificity and sensitivity. This model can predict the overall evaluation attitude of freshmen towards the course based on the dataset. In addition, we can further analyse the potential correlations between factors that enhance the intellectual and political content of the course. This model can further refine the evaluation of ideological and political courses by teachers and students in our school, thereby achieving the fundamental goal of moral cultivation.

An enhanced machine learning approach for effective prediction of IgA nephropathy patients with severe proteinuria based on clinical data

IgA Nephropathy (IgAN) is a disease of the glomeruli that may eventually lead to chronic kidney disease or kidney failure. The signs and symptoms of IgAN nephropathy are usually not specific enough and are similar to those of other glomerular or inflammatory diseases. This makes a correct diagnosis more difficult. This study collected data from a sample of adult patients diagnosed with primary IgAN at the First Affiliated Hospital of Wenzhou Medical University, with proteinuria ≥1 g/d at the time of diagnosis. Based on these samples, we propose a machine learning framework based on weIghted meaN oF vectOrs (INFO). An enhanced COINFO algorithm is proposed by merging INFO, Cauchy Mutation (CM) and Oppositional-based Learning (OBL) strategies. At the same time, COINFO and Support Vector Machine (SVM) were integrated to construct the BCOINFO-SVM framework for IgAN diagnosis and prediction. Initially, the proposed enhanced COINFO is evaluated using the IEEE CEC2017 benchmark problems, with the outcomes demonstrating its efficient optimization capability and accuracy in convergence. Furthermore, the feature selection capability of the proposed method is verified on the public medical datasets. Finally, the auxiliary diagnostic experiment was carried out through IgAN real sample data. The results demonstrate that the proposed BCOINFO-SVM can screen out essential features such as High-Density Lipoprotein (HDL), Uric Acid (UA), Cardiovascular Disease (CVD), Hypertension and Diabetes. Simultaneously, the BCOINFO-SVM model achieves an accuracy of 98.56%, with sensitivity at 96.08% and specificity at 97.73%, making it a potential auxiliary diagnostic model for IgAN.

A hybrid multimodal machine learning model for Detecting Alzheimer's disease

Alzheimer's disease (AD) diagnosis utilizing single modality neuroimaging data has limitations. Multimodal fusion of complementary biomarkers may improve diagnostic performance. This study proposes a multimodal machine learning framework integrating magnetic resonance imaging (MRI), positron emission tomography (PET) and cerebrospinal fluid (CSF) assays for enhanced AD characterization. The model incorporates a hybrid algorithm combining enhanced Harris Hawks Optimization (HHO) algorithm referred to as ILHHO, with Kernel Extreme Learning Machine (KELM) classifier for simultaneous feature selection and classification. ILHHO enhances HHO's search efficiency by integrating iterative mapping (IM) to improve population diversity and local escaping operator (LEO) to balance exploration-exploitation. Comparative analysis with other improved HHO algorithms, classic meta-heuristic algorithms (MHAs), and state-of-the-art MHAs on IEEE CEC2014 benchmark functions indicates that ILHHO achieves superior optimization performance compared to other comparative algorithms. The synergistic ILHHO-KELM model is evaluated on 202 AD Neuroimaging Initiative (ADNI) subjects. Results demonstrate superior multimodal classification accuracy over single modalities, validating the importance of fusing heterogeneous biomarkers. MRI + PET + CSF achieves 99.2 % accuracy for AD vs. normal control (NC), outperforming conventional and proposed methods. Discriminative feature analysis provides further insights into differential AD-related neurodegeneration patterns detected by MRI and PET. The differential PET and MRI features demonstrate how the two modalities provide complementary biomarkers. The neuroanatomical relevance of selected features supports ILHHO-KELM's potential for extracting sensitive AD imaging signatures. Overall, the study showcases the advantages of capitalizing on complementary multimodal data through advanced feature learning techniques for improving AD diagnosis.

An Enhanced RIME Optimizer with Horizontal and Vertical Crossover for Discriminating Microseismic and Blasting Signals in Deep Mines

Real-time monitoring of rock stability during the mining process is critical. This paper first proposed a RIME algorithm (CCRIME) based on vertical and horizontal crossover search strategies to improve the quality of the solutions obtained by the RIME algorithm and further enhance its search capabilities. Then, by constructing a binary version of CCRIME, the key parameters of FKNN were optimized using a binary conversion method. Finally, a discrete CCRIME-based BCCRIME was developed, which uses an S-shaped function transformation approach to address the feature selection issue by converting the search result into a real number that can only be zero or one. The performance of CCRIME was examined in this study from various perspectives, utilizing 30 benchmark functions from IEEE CEC2017. Basic algorithm comparison tests and sophisticated variant algorithm comparison experiments were also carried out. In addition, this paper also used collected microseismic and blasting data for classification prediction to verify the ability of the BCCRIME-FKNN model to process real data. This paper provides new ideas and methods for real-time monitoring of rock mass stability during deep well mineral resource mining.

Face Image Segmentation Using Boosted Grey Wolf Optimizer

Image segmentation methods have received widespread attention in face image recognition, which can divide each pixel in the image into different regions and effectively distinguish the face region from the background for further recognition. Threshold segmentation, a common image segmentation method, suffers from the problem that the computational complexity shows exponential growth with the increase in the segmentation threshold level. Therefore, in order to improve the segmentation quality and obtain the segmentation thresholds more efficiently, a multi-threshold image segmentation framework based on a meta-heuristic optimization technique combined with Kapur's entropy is proposed in this study. A meta-heuristic optimization method based on an improved grey wolf optimizer variant is proposed to optimize the 2D Kapur's entropy of the greyscale and nonlocal mean 2D histograms generated by image computation. In order to verify the advancement of the method, experiments compared with the state-of-the-art method on IEEE CEC2020 and face image segmentation public dataset were conducted in this paper. The proposed method has achieved better results than other methods in various tests at 18 thresholds with an average feature similarity of 0.8792, an average structural similarity of 0.8532, and an average peak signal-to-noise ratio of 24.9 dB. It can be used as an effective tool for face segmentation.

An optimized machine learning model for predicting hospitalization for COVID-19 infection in the maintenance dialysis population

COVID-19 has a high rate of infection in dialysis patients and poses a serious risk to human health. Currently, there are no dialysis centers in China that have analyzed the prevalence of COVID-19 infection in dialysis patients and the mortality rate. Although machine learning-based disease prediction methods have proven to be effective, redundant attributes in the data and the interpretability of the predictive models are still worth investigating. Therefore, this paper proposed a wrapper feature selection classification model to achieve the prediction of the risk of COVID-19 infection in dialysis patients. The method was used to optimize the feature set of the sample through an enhanced JAYA optimization algorithm based on the dispersed foraging strategy and the greedy levy mutation strategy. Then, the proposed method combines fuzzy K-nearest neighbor for classification prediction. IEEE CEC2014 benchmark function experiments as well as prediction experiments on the uremia dataset are used to validate the proposed model. The experimental results showed that the proposed method has a high prediction accuracy of 95.61% for the prevalence risk of COVID-19 infection in dialysis patients. Furthermore, it was shown that proalbumin, CRP, direct bilirubin, hemoglobin, albumin, and phosphorus are of great value for clinical diagnosis. Therefore, the proposed method can be considered as a promising method.

Intradialytic hypotension prediction using covariance matrix-driven whale optimizer with orthogonal structure-assisted extreme learning machine

Intradialytic hypotension (IDH) is an adverse event occurred during hemodialysis (HD) sessions with high morbidity and mortality. The key to preventing IDH is predicting its pre-dialysis and administering a proper ultrafiltration prescription. For this purpose, this paper builds a prediction model (bCOWOA-KELM) to predict IDH using indices of blood routine tests. In the study, the orthogonal learning mechanism is applied to the first half of the WOA to improve the search speed and accuracy. The covariance matrix is applied to the second half of the WOA to enhance the ability to get out of local optimum and convergence accuracy. Combining the above two improvement methods, this paper proposes a novel improvement variant (COWOA) for the first time. More, the core of bCOWOA-KELM is that the binary COWOA is utilized to improve the performance of the KELM. In order to verify the comprehensive performance of the study, the paper sets four types of comparison experiments for COWOA based on 30 benchmark functions and a series of prediction experiments for bCOWOA-KELM based on six public datasets and the HD dataset. Finally, the results of the experiments are analyzed separately in this paper. The results of the comparison experiments prove fully that the COWOA is superior to other famous methods. More importantly, the bCOWOA performs better than its peers in feature selection and its accuracy is 92.41%. In addition, bCOWOA improves the accuracy by 0.32% over the second-ranked bSCA and by 3.63% over the worst-ranked bGWO. Therefore, the proposed model can be used for IDH prediction with future applications.