Machine Model Research Articles

Prediction of implant failure risk due to periprosthetic femoral fracture after primary elective total hip arthroplasty : a simplified and validated model based on 154,519 total hip arthroplasties from the Swedish Arthroplasty Register.

While cementless fixation offers potential advantages over cemented fixation, such as a shorter operating time, concerns linger over its higher cost and increased risk of periprosthetic fractures. If the risk of fracture can be forecasted, it would aid the shared decision-making process related to cementless stems. Our study aimed to develop and validate predictive models of periprosthetic femoral fracture (PPFF) necessitating revision and reoperation after elective total hip arthroplasty (THA). We included 154,519 primary elective THAs from the Swedish Arthroplasty Register (SAR), encompassing 21 patient-, surgical-, and implant-specific features, for model derivation and validation in predicting 30-day, 60-day, 90-day, and one-year revision and reoperation due to PPFF. Model performance was tested using the area under the curve (AUC), and feature importance was identified in the best-performing algorithm. The Lasso regression excelled in predicting 30-day revisions (area under the receiver operating characteristic curve (AUC) = 0.85), while the Gradient Boosting Machine (GBM) model outperformed other models by a slight margin for all remaining endpoints (AUC range: 0.79 to 0.86). Predictive factors for revision and reoperation were identified, with patient features such as increasing age, higher American Society of Anesthesiologists grade (> III), and World Health Organization obesity classes II to III associated with elevated risks. A preoperative diagnosis of idiopathic necrosis increased revision risk. Concerning implant design, factors such as cementless femoral fixation, reverse-hybrid fixation, hip resurfacing, and small (< 35 mm) or large (> 52 mm) femoral heads increased both revision and reoperation risks. This is the first study to develop machine-learning models to forecast the risk of PPFF necessitating secondary surgery. Future studies are required to externally validate our algorithm and assess its applicability in clinical practice.

Predicting Polymerase Chain Reaction Success: Integrating the K-Word Order Model, Physicochemical Properties Modeling of Double Bases, and Support Vector Machine.

Polymerase Chain Reaction (PCR) has been a pivotal scientific technique since the twentieth century, and it is widely applied across various domains. Despite its ubiquity, challenges persist in efficiently amplifying specific DNA templates. While PCR experimental procedures have garnered significant attention, the analysis of the DNA template, which is the experiment's focal point, has been notably overlooked. This study addresses the uncertainty surrounding the amplification of DNA fragments using conventional Taq DNA polymerase-based PCR protocols. The imperative need to characterize DNA templates and devise a reliable method for predicting PCR success is underscored. In this study, we formulate a 72-dimensional feature vector representing a DNA template through the utilization of k-word order and modeling of physicochemical properties of double bases. Subsequently, a Support Vector Machine (SVM) model is employed to assess PCR results. A jackknife cross-validation test is used to evaluate the anticipated success rates, resulting in an overall accuracy of 95.77%. Sensitivity, specificity, and Matthew's Correlation Coefficient (MCC) stand at 95.75%, 95.79%, and 0.915, respectively.

Machine Learning Analysis of Glutamate Receptor Activity in Developing Locus Coeruleus Neurons

: The developing brain undergoes a remarkable process of synapse production and maturation, particularly in glutamatergic synapses. In this study, we focused on the locus coeruleus (LC) nucleus, a brain region crucial for cognitive functions, to investigate the developmental changes in glutamatergic synaptic connections. Using the whole-cell patch clamp method, we recorded evoked excitatory postsynaptic currents (eEPSCs) from LC neurons in rats at ages 7, 14, and 21 days. To assess the developmental changes, we employed fractal analysis to compute fractal dimensions of AMPA and NMDA receptors, serving as markers for synaptic maturation. Our findings revealed a significant increase in fractal dimensions during the third postnatal week, indicating a developmental progression in the complexity and organization of synaptic connections. Pearson correlation analysis demonstrated a strong positive correlation between amplitude and fractal dimensions (p < 0.001), suggesting that the synaptic currents' amplitude is closely related to the fractal properties of the receptors. Furthermore, linear regression analysis showed a linear relationship between fractal dimensions and age (R2 = 0.843, p < 0.001), indicating that fractal analysis can be a robust tool for predicting age-dependent developmental changes. Additionally, we employed machine learning techniques to predict developmental changes based on AMPA and NMDA receptors. Support Vector Machine (SVM) models outperformed random forest models in accurately predicting age-dependent developmental changes, as indicated by the area under the curve (AUC) values. SVM achieved an AUC of 0.89 for AMPA receptors and 0.86 for NMDA receptors, demonstrating the effectiveness of fractal-based features in characterizing synaptic maturation. This study offers valuable insights into synaptic development in the LC nucleus and demonstrates the potential of fractal analysis as a tool to understand brain plasticity and early development. Fractal dimensions play a crucial role in characterizing the maturation of glutamatergic synapses, providing a foundation for further research in neural circuitry development.

Dysfunctional large-scale brain networks in drug-naïve depersonalization-derealization disorder patients

BackgroundDepersonalization-Derealization Disorder (DPRD) presents challenges in understanding its neurobiological underpinnings. Several neuroimaging studies have revealed altered brain function and structure in DPRD. However, the knowledge about large-scale dysfunctional brain networks in DPRD remains unknown.MethodsA total of 47 drug-naïve DPRD patients and 49 healthy controls (HCs) were recruited and underwent resting-state functional scanning. After constructing large-scale brain networks, we calculated within‐and between‐network functional connectivity (FC) using the Schaefer and Tian atlas. The Support Vector Machine (SVM) model was employed to classify DPRD patients and provide features for DPRD patients concerning the dysfunctional large-scale brain networks. Finally, the correlation analysis was performed between altered functional connectivity of large‐scale brain networks and scores of clinical assessments in DPRD patients.ResultsCompared to HCs, we found significantly decreased FCs, within-networks across four brain networks and between-networks involving 18 pairs of brain networks in DPRD patients. Moreover, our results revealed a satisfactory classification accuracy (80%) of these decreased FCs for correctly identifying DPRD patients. Notably, a significant negative correlation was observed between the 'Self' factor of the CDS and the FC within the somatosensory-motor network.ConclusionOverall, disrupted FC of large-scale brain networks may contribute to understanding neurobiological underpinnings in DPRD. Our findings may provide potential targets for therapeutic interventions.

Adaptive Sampling Limit Surface Search Application to Identify Cliff-Edge Effects in Severe Accident Uncertainty Analysis

Severe accident (SA) codes and their core degradation models have to deal with strongly nonlinear and discontinuous phenomena. In the application of uncertainty quantification to SA simulations, the combination of such phenomena may lead to a strong increase in the uncertainty propagated through the simulation, as well as to the chaotic behavior of the output variables. In this framework, the application of the limit surface search method of the RAVEN tool is proposed for a case where cliff-edge effects of SA phenomena determine a bifurcation of an output figure of merit. The algorithm is based on a predictive method making use of a support vector machine model, and it is applied with the aim of separating those input values that lead to different phenomenologies among the uncertainty calculations. The case study is in regard to the uncertainty analysis of the ASTEC code simulation of the QUENCH6 experimental test conducted in the framework of the International Atomic Energy Agency Coordinated Research Project I31033.

Prediction of Prostate Cancer From Routine Laboratory Markers With Automated Machine Learning.

In this study, we attempted to select the optimum cases for a prostate biopsy based on routine laboratory test results in addition to prostate-specific antigen (PSA) blood test using H2O automated machine learning (AutoML) software, which includes many common machine learning algorithms. The study included 737 patients (46-88 years old). Routine laboratory measurements were used to train machine learning models using H2O AutoML. We created a model that classifies prostate biopsy results as malignant or benign. The performance of the best model was evaluated using the area under the receiver operating characteristic curve (AUC), log-loss metric, F1 score, positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity. The model's performance was evaluated through the SHapley Additive exPlanations (SHAP) analysis feature-based interpretation method applied to comprehend the machine learning model. The gradient boosting machine model was the most successful. The best result was obtained in the model with 11 parameters, including PSA, free PSA, free PSA to PSA, hemoglobin, neutrophils, platelets, neutrophil-to-lymphocyte ratio (NLR), glucose, platelet-to-lymphocyte ratio (PLR), lymphocytes, and age. The AUC of this model was 0.72, the specificity was 0.84, the PPV was 0.65, the NPV was 0.69, and the accuracy was 0.68. Our results suggest that adding only routine laboratory parameters to the PSA test and developing machine learning algorithms can help reduce the number of unnecessary prostate biopsies without overlooking the diagnosis of PCa.

Computer-aided drug design approaches for the identification of potent inhibitors targeting elongation factor G of Mycobacterium tuberculosis.

Elongation factor G (EF-G) is essential for protein synthesis in Mycobacterium tuberculosis (Mtb), positioning it as a promising target for anti-tubercular drug development. This study employs Structure-Based Drug Design (SBDD) to identify potential small molecule inhibitors that specifically target EF-G. Initially, binding hotspots on EF-G were pinpointed, and the binding modes of various compounds were analyzed. Through protein-protein interaction studies, several promising candidates were validated. Virtual screening and molecular docking techniques were utilized to evaluate the binding affinities and interactions of 20 candidate molecules with Mtb EF-G. Additionally, toxicity profiles of these compounds were assessed using predictive models, which indicated non-carcinogenic properties. To further refine the selection process, Support Vector Machine (SVM) and Random Forest models were applied to predict cell wall permeability. Notably, Asinex (8853) and Asinex (102619) emerged as top candidates, boasting high probability scores for effective permeability. Molecular docking and molecular dynamics (MD) simulations revealed that Asinex (8853), Asinex (102619), and Otava (79226) exhibited strong binding affinities and favorable conformations within the active site of Mtb EF-G. These findings suggest that these compounds have significant potential as inhibitors, warranting further investigation into their efficacy as novel anti-tubercular agents. Overall, this study emphasizes the value of Structure-Based Drug Design in identifying promising therapeutic candidates against tuberculosis by targeting essential bacterial factors like EF-G.

Association Between BMI and Neurocognitive Functions Among Middle-aged Obese Adults: Preliminary Findings Using Machine-learning (ML)-based Approach.

Studies suggest that obesity predisposes individuals to developing cognitive dysfunction and an increased risk of dementia, but the nature of the relationship remains largely unexplored for better prognostic predictors. This study, the first of its kind in Indian participants with obesity, was intended to explore the use of quantification of different neurocognitive indices with increasing body mass index (BMI) among middle-aged participants with obesity. Additionally, machine-learning models were used to analyse the predictive performance of BMI for different cognitive functions. In the cross-sectional analytical study, a total of 137 (n = 137) participants were included. Out of the total, 107 healthy obese (BMI = 23.0-30.0 kg m-2; age between 36 and 55 years of both genders) were recruited from the out-patient department of the Department of Endocrinology and General Medicine, and 30 participants were recruited as the control group, between March 2023 to February 2024. The participants underwent neuropsychological assessments, including mini-mental state examination (MMSE), Montreal cognitive assessment (MoCA) and serum levels of brain-derived neurotrophic factor (BDNF). Significant (p < .05) differences were observed for neurocognitive functions for the obese group versus the control group. According to the correlation heatmaps, BMI was significantly (p < .05) negatively associated with BDNF. Multivariate linear regression analysis revealed a substantial (p < .05) decline in BDNF with a change in BMI, accenting its significant impact on cognitive ageing. Additionally, consistent decreasing trends were observed across the MoCA and MMSE, confirming the robustness of the findings across diverse analytical methodologies. Furthermore, the linear regression model and super vector machine model contributed additional evidence to the consistency of the trends in cognitive decline linked to BMI variations. The preliminary results of the present study support that increased BMI is an important physiological indicator that influences neurocognition and neuroplasticity in individuals with obesity.

An oral microbiota-based deep neural network model for risk stratification and prognosis prediction in gastric cancer

ABSTRACT Background This study aims to develop an oral microbiota-based model for gastric cancer (GC) risk stratification and prognosis prediction. Methods Oral microbial markers for GC prognosis and risk stratification were identified from 99 GC patients, and their predictive potential was validated on an external dataset of 111 GC patients. The identified bacterial markers were used to construct a Deep Neural Network (DNN) model, a Random Forest (RF) model, and a Support Vector Machine (SVM) model for predicting GC prognosis. Results GC patients with <3 years of survival showed a higher abundance of Aggregatibacter and diminished abundances of Filifactor and Moryella than those who survived ≥3 years. The Boruta algorithm unearthed Leptotrichia as another significant marker for GC prognosis. Consequently, a DNN model was constructed based on the relative abundances of these bacteria, predicting 3-year and 5-year survival in GC patients with Area Under Curve of 0.814 and 0.912, respectively. Notably, the DNN model outperformed the TNM staging system, SVM and RF models. The prognostic value of these bacterial markers was further reinforced by external validation. Conclusion The oral microbiota-based DNN model may advance GC prognosis. The biological functions of these oral bacterial markers warrant further investigation from the perspective of GC progression.

Bidimensional Increment Entropy for Texture Analysis: Theoretical Validation and Application to Colon Cancer Images.

Entropy algorithms are widely applied in signal analysis to quantify the irregularity of data. In the realm of two-dimensional data, their two-dimensional forms play a crucial role in analyzing images. Previous works have demonstrated the effectiveness of one-dimensional increment entropy in detecting abrupt changes in signals. Leveraging these advantages, we introduce a novel concept, two-dimensional increment entropy (IncrEn2D), tailored for analyzing image textures. In our proposed method, increments are translated into two-letter words, encoding both the size (magnitude) and direction (sign) of the increments calculated from an image. We validate the effectiveness of this new entropy measure by applying it to MIX2D(p) processes and synthetic textures. Experimental validation spans diverse datasets, including the Kylberg dataset for real textures and medical images featuring colon cancer characteristics. To further validate our results, we employ a support vector machine model, utilizing multiscale entropy values as feature inputs. A comparative analysis with well-known bidimensional sample entropy (SampEn2D) and bidimensional dispersion entropy (DispEn2D) reveals that IncrEn2D achieves an average classification accuracy surpassing that of other methods. In summary, IncrEn2D emerges as an innovative and potent tool for image analysis and texture characterization, offering superior performance compared to existing bidimensional entropy measures.

Predictive modeling of transverse cracking in continuously reinforced concrete pavement: a machine learning approach

Abstract Accurate prediction of transverse cracking in Continuously Reinforced Concrete Pavement (CRCP) is critical for improving infrastructure management procedures and preserving the road network's long-term durability and safety. This paper conducts a thorough analysis into predicting transverse cracking in CRCP using machine learning approaches. The research involved meticulous data preparation, feature selection, and evaluation of various machine learning models to identify the most effective predictor. Key variables such as pavement age, total thickness, temperature, freeze index, traffic volume, precipitation, and initial International Roughness Index (IRI) were analyzed for their impact on transverse cracking occurrences. Sensitivity analysis was conducted to assess the influence of individual input variables on model predictions. Results indicated that the cubic Support Vector Machine (SVM) model outperformed other models, demonstrating exceptional predictive accuracy. Furthermore, sensitivity analysis revealed significant correlations between input variables and transverse cracking occurrences, emphasizing the importance of considering a holistic range of factors in pavement engineering and maintenance strategies. Our findings, which provide insights into the intricate interactions between input factors and pavement distress, help to create tailored treatments and methods for minimizing transverse cracking and enhancing CRCP performance.&amp;#xD;

Unraveling the Therapeutic Potential of Sophora flavescens Aiton in Myocardial Infarction: An Integrative Approach Combining Bioinformatics, Network Pharmacology, and Experimental Validation.

This study aims to elucidate the relationship between potential MI targets and SFA's mechanism of action, providing a theoretical basis for clinical development of new drugs. Myocardial infarction (MI) has been identified as one of the major cardiovascular diseases with adverse consequences. Sophora flavescens Aiton (SFA) is indicated for the therapeutic treatment of MI. However, there is no systematic research on the new therapeutic targets for MI and the exact action mechanism of SFA. This study explores the potential mechanisms of SFA in treating MI by integrating bioinformatics, network pharmacology analyses and experimental verification. New MI targets were predicted using bioinformatics techniques. Network pharmacology and molecular docking jointly served for predicting the key targets and underlying mechanisms of SFA. A machine learning model was developed to identify the core MI targets. Subsequently, H9c2 cardiomyocytes hypoxia model was established for experimental verification. 140 active components were ascertained in SFA and 59 differentially expressed genes (DEGs) were screened for MI. Eighty-seven shared genes were obtained by WGCAN. Eighty proteins and 413 interactions were identified by PPI network. After building the machine model, three core targets were identified (STAT1, TNFRSF1A and MCL1). According to in vitro experiments, SFA exerts a protective effect relying on three core targets and biological processes, including cell viability, the inflammatory response, and antiapoptotic effects, etc. Conclusion: This study finds new core targets for MI and the therapeutic activity of SFA against MI, of which the experimental verification provides valuable insights into the molecular mechanisms underlying SFA's efficacy in MI treatment and paves the way for targeted drug development strategies.

Development of a disease diagnostic model to predict the occurrence of central precocious puberty of female.

To develop a clinical model for predicting the occurrence of Central Precocious Puberty based on the breast development outcomes in chinese girls. This is a retrospective study, which included a total of 1,001 girls aged 6-9 years old who visited the outpatient clinic of Beijing Children's Hospital from January 2017 to October 2022 for "breast development". Participants were categorized into pubertal development (PD) cohort and simple premature breast development (PT) according to the criteria, and information was collected and tested for relevant indicators. After dealing with missing data, logistic regression, LASSO regression and random forest were used to screen the variables, and support vector machine models were built with SMOTE oversampling and ten-fold cross-validation to assess the effectiveness of the models in the training and validation sets. 1,001 girls were included in the analysis, of whom 369 (36.9 %) were diagnosed with PD and 632 (63.1 %) with PT. Body mass index (BMI), bone age (BA), luteinizing hormone (LH), follicle stimulating hormone (FSH), estradiol (E2), uterine diameter, and ovary volume were identified as the final predictor variables by three variable screening methods. The AUC of the constructed disease diagnostic model was 0.9457 in the developmental cohort and 0.8357 inthe external validation group, and sensitivity analyses revealed that the performance of the constructed models with different variable selection strategies was similar. A disease diagnostic model was developed that may help predict a girl's risk of diagnosing central precocious puberty.

Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer Screening Among Populations in the United States: Machine Learning Approach.

Breast cancer screening plays a pivotal role in early detection and subsequent effective management of the disease, impacting patient outcomes and survival rates. This study aims to assess breast cancer screening rates nationwide in the United States and investigate the impact of social determinants of health on these screening rates. Data on mammography screening at the census tract level for 2018 and 2020 were collected from the Behavioral Risk Factor Surveillance System. We developed a large-scale dataset of social determinants of health, comprising 13 variables for 72,337 census tracts. Spatial analysis employing Getis-Ord Gi statistics was used to identify clusters of high and low breast cancer screening rates. To evaluate the influence of these social determinants, we implemented a random forest model, with the aim of comparing its performance to linear regression and support vector machine models. The models were evaluated using R2 and root mean squared error metrics. Shapley Additive Explanations values were subsequently used to assess the significance of variables and direction of their influence. Geospatial analysis revealed elevated screening rates in the eastern and northern United States, while central and midwestern regions exhibited lower rates. The random forest model demonstrated superior performance, with an R2=64.53 and root mean squared error of 2.06, compared to linear regression and support vector machine models. Shapley Additive Explanations values indicated that the percentage of the Black population, the number of mammography facilities within a 10-mile radius, and the percentage of the population with at least a bachelor's degree were the most influential variables, all positively associated with mammography screening rates. These findings underscore the significance of social determinants and the accessibility of mammography services in explaining the variability of breast cancer screening rates in the United States, emphasizing the need for targeted policy interventions in areas with relatively lower screening rates.

Artificial intelligence models using F-wave responses predict amyotrophic lateral sclerosis.

Nerve conduction F-wave studies contain critical information about subclinical motor dysfunction which may be used to diagnose patients with amyotrophic lateral sclerosis (ALS). However, F-wave responses are highly variable in morphology, making waveform interpretation challenging. Artificial Intelligence techniques can extract time-frequency features to provide new insights into ALS diagnosis and prognosis. A retrospective analysis was performed on F-wave responses from 46,802 patients. Discrete wavelet transforms were applied to time-series waveform responses after stimulating ulnar, median, fibular, and tibial nerves. Wavelet coefficient statistics, onset age, sex, and BMI were features for training a Gradient Boosting Machine model on 40,095 (5,329 diagnosed with motor neuron disease). Model performance was tested on responses from 689 ALS patients meeting Gold Coast criteria and 689 age- and sex-matched controls. An exploratory analysis examined model performance on cohorts of patients with inclusion body myositis (IBM), cervical radiculopathy, lumbar radiculopathy, or peripheral neuropathy which can mimic ALS symptoms. Factors affecting survival were estimated through cox proportional hazards regression. The model trained using wavelet-features on the full waveform had 90% recall, 87% precision, and 88% accuracy. Similar model performance was measured using features only from the M-Wave or F-Wave. Classification probabilities for ALS patients were statistically different from the diagnoses mimicking ALS symptoms (p<0.001, ANOVA, Tukey's post-hoc), Higher model classification probabilities of ALS, older age at onset, and family history of ALS alone or with frontotemporal dementia were factors decreasing survival. Longer diagnostic delay and upper limb onset site were factors increasing survival. Model scores two standard deviations below the mean had 4 months increased survival (two standard deviations below had 3 months decreased survival). Artificial intelligence techniques extracted important information from F-wave responses to estimate a patient's likelihood of ALS and their survival risks. Although the model can make predictions at specific decision threshold as presented here, the true strength of such a model lies in its ability to provide probabilities about whether a patient is likely to have ALS compared to other mimicking diagnoses such as IBM, cervical or lumbar radiculopathy, or peripheral neuropathy. These probabilities provide clinicians with additional information they can use to make the final diagnosis with greater confidence and precision. Integrating such a model into the clinical workflow could help clinicians diagnose ALS sooner and manage treatment based on estimated survival, which may improve outcomes and patients' quality of life.

NDUFA11 may be the disulfidptosis-related biomarker of ischemic stroke based on integrated bioinformatics, clinical samples, and experimental analyses

BackgroundProgrammed cell death plays an important role in neuronal injury and death after ischemic stroke (IS), leading to cellular glucose deficiency. Glucose deficiency can cause abnormal accumulation of cytotoxic disulfides, resulting in disulfidptosis. Ferroptosis, apoptosis, necroptosis, and autophagy inhibitors cannot inhibit this novel programmed cell death mechanism. Nevertheless, the potential mechanisms of disulfidptosis in IS remain unclear.MethodsThe GSE16561 dataset was used to screen for differentially expressed disulfidptosis-related biomarkers (DE-DRBs). A correlation between the DE-DRBs was detected. The optimal machine-learning (ML) model and predictor molecules were determined. The GSE58294 dataset was used to verify the accuracy of the optimal ML model. The DE-DRB expression was detected in the blood of patients with IS. Based on IS models, experimental analyses were performed to verify DE-DRB expression and the correlation between DE-DRBs.ResultsLeucine-rich pentatricopeptide repeat-containing (LRPPRC) and NADH dehydrogenase [ubiquinone] 1 alpha subcomplex subunit 11 (NDUFA11) were identified as DE-DRBs. The NADH: ubiquinone oxidoreductase core subunit S1 (NDUFS1) interacted with NDUFA11 and LRPPRC. The support vector machine (SVM) model was identified as the optimal ML model. The NDUFA11 expression level in the blood of patients with IS was 20.9% compared to that in normal controls. NDUFA11 expression was downregulated in the in vitro/in vivo models of IS. The number of formed complexes of NDUFS1 and NDUFA11 decreased in the in vitro/in vivo models of IS.ConclusionThis research suggests that NDUFA11 is a specific DRB for IS and demonstrates alterations in the disulfidptosis-related protein complexes NDUFS1-NDUFA11.

Differentiation between multiple sclerosis and neuromyelitis optic spectrum disorders with multilevel fMRI features: A machine learning analysis

The conventional statistical approach for analyzing resting state functional MRI (rs-fMRI) data struggles to accurately distinguish between patients with multiple sclerosis (MS) and those with neuromyelitis optic spectrum disorders (NMOSD), highlighting the need for improved diagnostic efficacy. In this study, multilevel functional metrics including resting state functional connectivity, amplitude of low frequency fluctuation (ALFF), and regional homogeneity (ReHo) were calculated and extracted from 116 regions of interest in the anatomical automatic labeling atlas. Subsequently, classifiers were developed using different combinations of these selected features to distinguish between MS and NMOSD. Compared to models based on individual MRI features, support vector machine (SVM) and logistic regression (LR) models that integrated multilevel functional features such as RSFC, ALFF, and ReHo demonstrated the highest levels of performance on the testing cohorts (SVM, AUC = 0.857; LR, AUC = 0.929). Adding structural features of gray matter volume (GMV) data did not notably improve the classification performance of the machine learning models using multilevel rs-fMRI features. Notably, similar trends were observed across different brain templates, with models based on RSFC, ALFF, and ReHo yielding optimal performance. These findings suggest that utilizing multilevel fMRI features can effectively differentiate between MS and NMOSD patients.

Classification Prediction of Hydrocephalus After Intercerebral Haemorrhage Based on Machine Learning Approach.

In order to construct a clinical classification prediction model for hydrocephalus after intercerebral haemorrhage(ICH) to guide clinical treatment decisions, this paper retrospectively analyses the clinical data of 844 cases of ICH and hydrocephalus inpatients admitted to Yueyang People's Hospital from May 2019 to October 2022, of which 95 cases of hydrocephalus occurred after ICH and no hydrocephalus in 749 cases. The following indicators were compared between the two groups of patients: gender, age, Glasgow Coma Scale(GCS)score, whether the amount of bleeding was greater than 30ml, whether it broke into the ventricle or not, modified Graeb score(MGS), modified Rankin Scale (MRS) score, whether surgery was performed or not, red blood cells, white blood cells, and platelets. After variable screening, the following six variables were selected: GCS score, MGS, MRS score, whether the bleeding volume was greater than 30ml, whether it broke into the ventricle or not, and whether surgery was performed or not were modelled and analysed using logistic regression model and support vector machine model in machine learning. The results showed that under the same conditions, the accuracy of the support vector machine model was 0.89 and F1 was 0.838 ,the value of the AUC of the support vector machine model is 0.888; the accuracy of the logistic regression model was 0.902 and F1 was 0.89, the value of the AUC of the support vector machine model is 0.903. Compared with the group without hydrocephalus, patients in the group with hydrocephalus had bleeding volume greater than 30ml, haemorrhage into the ventricles of the brain, and had undergone surgery in the brain, and the difference was statistically significant (P 0.001). Statistical analysis showed that GCS score ≤ 8.8, modified Graeb score (MGS) ≥ 10 and MRS score ≥ 3 were independent risk factors for the development of hydrocephalus after spontaneous ventricular haemorrhage. Therefore, patients with lower GCS score, higher modified Graeb score, higher MRS score, bleeding volume > 30ml, haemorrhage into the ventricles of the brain, and experience of having undergone surgery in the brain should be operated on early to remove the intraventricular haematoma in order to reduce the incidence of hydrocephalus.

A Systematic Comparative Study on the use of Machine Learning Techniques to Predict Lung Cancer and its Metastasis to the Liver: LCLM-Predictor Model

Lung cancer is one of the major causes of cancer deaths with thousands of affected patients who have developed liver metastasis, complicating the treatment and further prognosis. Early predictions of lung cancer and metastasis may greatly improve patient outcomes since clinical interventions will be instituted in time. This paper compares the performance of different machine learning models including Decision Tree Classifiers, Logistic Regression, Naïve Bayes, K-Nearest Neighbors, Support Vector Machines and Gaussian Mixture Models toward the best set of techniques for prediction. The applied dataset includes various clinical features, such as respiratory symptoms and biochemical markers, for the development of stronger predictive performance. The models were cross-validated using testing and validation techniques aimed at generalizing the whole model with reliability in generating both train and test data. The results of the generated models are gauged using metrics of accuracy, precision, recall, F1-score, and area under ROC curve. Results obtained have revealed that the Decision Tree and KNN models also showed stronger predictive accuracy and strong classification performance, especially in early-stage lung cancer and liver metastasis. The present study is a comparison of the Decision Tree and KNN models, which hence denotes the potential of these models in clinical decision-making and suggests application to the development of diagnostic tools for the early detection of cancer. This provides a very useful guide that is applicable in the use of machine learning in oncology and helps pave the way to future research which would be focused on model optimization and integration into healthcare systems that would produce better management of patients and better survival rates.

Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning

The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired pneumonia (SCAP) in the intensive care unit (ICU). We collected data from two centers as the development and external validation cohorts. Variables were screened using the Recursive Feature Elimination method. Five machine learning algorithms were used to build predictive models. Models were evaluated through nested cross-validation to select the best one. The model was interpreted using Shapley Additive Explanations. We selected the optimal model to generate the web calculator. A total of 23 predictive features were selected. The Light Gradient Boosting Machine (LightGBM) model had an area under the receiver operating characteristic curve (AUC) of 0.842 (95% CI: 0.757–0.927), with an external 5-fold cross-validation average AUC of 0.842 ± 0.038, which was superior to the other models. External validation results also demonstrated good performance by the LightGBM model with an AUC of 0.856 (95% CI: 0.792–0.921). Based on this, we generated a web calculator by combining five high importance predictive factors. The LightGBM model was confirmed to be efficient and stable in predicting the mortality risk of patients with SCAP admitted to the ICU. The web calculator based on the LightGBM model can provide clinicians with a prognostic evaluation tool.