Gradient Boosting Machine Learning Research Articles

Body mass index and long-term survival following acute pulmonary embolism: insights from machine learning models

Abstract Funding Acknowledgements None. Introduction Body mass index (BMI) has been associated with long-term survival in the setting of acute pulmonary embolism (PE) [1,2]. The non-linear pattern of this association proves to be challenging for frequentist statistical analyses. Contemporary machine learning models offer superior handling capabilities of datasets with non-linear data distributions. Purpose To examine the predictive power of BMI expressed as a continuous variable in a dataset of patients with pulmonary embolism (PE) using the extreme gradient boosting (XGBoost) machine learning framework. Methods All consecutive patients with confirmed PE admitted to the two tertiary teaching Clinical Hospitals in the metropolitan area from November 2013 until November 2018 were included in the study. The diagnosis was established by multislice computed tomography angiography. Machine learning models were investigated using the LightGBM variant of XGBoost. We allocated 80% of the dataset for model training and the remaining 20% for testing. Two models were tested: (1) a model with 20 variables tailored according to the Cox proportional hazards model in the initial publication; (2) a model with 11 independent predictors of outcome. Results The study population is described in detail in previous publications [1,2]. In brief, a total of 761 patients were included in the study. The population was predominantly female (57.4%), aged 73 (61-81). The median overall follow-up was 675 days (114-1331). There were 335 (44.0%) cases of death recorded at follow-up. A Cox proportional hazards model with 19 covariates significantly predicted death during follow-up (χ2 = 595.5, p<0.001). The first BMI quartile was an independent predictor of death (hazard ratio 1.66, 95% confidence interval 1.31-2.10). However, when expressed as a continuous variable, BMI failed to enter the model. In both XGBoost models, BMI expressed as a continuous variable proved to be a feature with high predictive power, second only to pulmonary embolism severity index score (Figure 1 and 2). The models showed high accuracy, with an area under curve of 0.840 and 0.864 respectively. Conclusion In the XGBoost analysis, a machine learning framework with superior handling of non-linear data, BMI expressed as a continuous variable emerged as the second strongest predictor of outcome among 761 PE patients.Figure 1Figure 2

Investigating disparities in smoking cessation treatment for veterans with multiple sclerosis: A national analysis.

Smoking is a risk factor for multiple sclerosis (MS) development, symptom burden, decreased medication efficacy, and increased disease-related mortality. Veterans with MS (VwMS) smoke at critically high rates; however, treatment rates and possible disparities are unknown. To promote equitable treatment, we aim to investigate smoking cessation prescription practices for VwMS across social determinant factors. We extracted data from the national Veterans Health Administration electronic health records between October 1, 2017, and September 30, 2018. To derive marginal estimates of the association of MS with receipt of smoking-cessation pharmacotherapy, we used propensity score matching through the extreme gradient boosting machine learning model. VwMS who smoke were matched with veterans without MS who smoke on factors including age, race, depression, and healthcare visits. To assess the marginal association of MS with different cessation treatments, we used logistic regression and conducted stratified analyses by sex, race, and ethnicity. The matched sample achieved a good balance across most covariates, compared to the pre-match sample. VwMS (n=3320) had decreased odds of receiving prescriptions for nicotine patches ([Odds Ratio]OR=0.86, p<.01), non-patch nicotine replacement therapy (NRT; OR=0.81, p<.001), and standard practice dual NRT (OR=0.77, p<.01), compared to matches without MS (n=13,280). Men with MS had lower odds of receiving prescriptions for nicotine patches (OR=0.88, p=.05), non-patch NRT (OR=0.77, p<.001), and dual NRT (OR=0.72, p<.001). Similarly, Black VwMS had lower odds of receiving prescriptions for patches (OR=0.62, p<.001), non-patch NRT (OR=0.75, p<.05), and dual NRT (OR=0.52, p<.01). The odds of receiving prescriptions for bupropion or varenicline did not differ between VwMS and matches without MS. VwMS received significantly less smoking cessation treatment, compared to matched controls without MS, showing a critical gap in health services as VwMS are not receiving dual NRT as the standard of care. Prescription rates were especially lower for male and Black VwMS, suggesting that under-represented demographic groups outside of the white female category, most often considered as the "traditional MS" group, could be under-treated regarding smoking cessation support. This foundational work will help inform future work to promote equitable treatment and implementation of cessation interventions for people living with MS.

Prediction and causal inference of hyperuricemia using gut microbiota.

Hyperuricemia (HUA) is a symptom of high blood uric acid (UA) levels, which causes disorders such as gout and renal urinary calculus. Prolonged HUA is often associated with hypertension, atherosclerosis, diabetes mellitus, and chronic kidney disease. Studies have shown that gut microbiota (GM) affect these chronic diseases. This study aimed to determine the relationship between HUA and GM. The microbiome of 224 men and 254 women aged 40years was analyzed through next-generation sequencing and machine learning. We obtained GM data through 16S rRNA-based sequencing of the fecal samples, finding that alpha-diversity by Shannon index was significantly low in the HUA group. Linear discriminant effect size analysis detected a high abundance of the genera Collinsella and Faecalibacterium in the HUA and non-HUA groups. Based on light gradient boosting machine learning, we propose that HUA can be predicted with high AUC using four clinical characteristics and the relative abundance of nine bacterial genera, including Collinsella and Dorea. In addition, analysis of causal relationships using a direct linear non-Gaussian acyclic model indicated a positive effect of the relative abundance of the genus Collinsella on blood UA levels. Our results suggest abundant Collinsella in the gut can increase blood UA levels.

CLDN18: Clinical, Pathological, and Genetic Signatures with Drug Screening in Gastric Adenocarcinoma.

The CLDN18 gene, encoding claudin 18.1 and claudin 18.2, is a key component of tight junction strands in epithelial cells that form a paracellular barrier that is critical in Stomach Adenocarcinoma (STAD). Our study included 1,095 patients with proven STAD, 415 from The Cancer Genome Atlas (TCGA) cohort and 680 from the Gene Expression Omnibus database. We applied various analyses, including gene set enrichment analysis, pathway analysis, and in vitro drug screening to evaluate survival, immune cells, and genes and gene sets associated with cancer progression, based on CLDN18 expression levels. Gradient boosting machine learning (70% for training, 15% for validation, and 15% for testing) was used to evaluate the impact of CLDN18 on survival and develop a survival prediction model. High CLDN18 expression correlated with worse survival in lymphocyte-poor STAD, accompanied by decreased helper T cells, altered metabolic genes, low necrosis-related gene expression, and increased tumor proliferation. CLDN18 expression showed associations with gene sets associated with various stomach, breast, ovarian, and esophageal cancers, while pathway analysis linked CLDN18 to immunity. Incorporating CLDN18 expression improved survival prediction in a machine learning model. Notably, nutlin-3a and niraparib effectively inhibited high CLDN18-expressing gastric cancer cells in drug screening. Our study provides a comprehensive understanding of the biological role of CLDN18-based bioinformatics and machine learning analysis in STAD, shedding light on its prognostic significance and potential therapeutic implications. To fully elucidate the molecular intricacies of CLDN18, further investigation is warranted, particularly through in vitro and in vivo studies.

Comprehensive Proteomic Profiling of Aqueous Humor in Idiopathic Uveitis and Vogt-Koyanagi-Harada Syndrome.

Idiopathic uveitis (IU) and Vogt-Koyanagi-Harada (VKH) syndrome are common types of uveitis. However, the exact pathological mechanisms of IU and VKH remain unclear. Proteomic analysis of aqueous humor (AH), the most easily accessible intraocular fluid and a key site of uveitis development, may reveal potential biomarkers and elucidate uveitis pathogenesis. In this study, 44 AH samples, including 12 IU cases, 16 VKH cases, and 16 controls, were subjected to label-free quantitative proteomic analysis. We identified 557 proteins from a comprehensive spectral library of 634 proteins across all samples. The AH proteomic profiles of the IU and VKH groups were different from those of the control group. Differential analysis revealed a shared pattern of extracellular matrix disruption and downregulation of retinal cellular proteins in the IU and VKH groups. Enrichment analysis revealed a protein composition indicative of inflammation in the AH of the IU and VKH groups but not in that of the control group. In the IU and VKH groups, innate immunity played an important role, as indicated by complement cascade activation and overexpression of innate immune cell markers. Extreme gradient boosting (XGBoost), an efficient and robust machine learning algorithm, was subsequently used to screen potential biomarkers for classifying the IU, VKH, and control groups. Transferrin and complement factor B were deemed the most important and represent a promising biomarker panel. These proteins were validated by high-resolution multiple reaction monitoring (HR-MRM) in an independent validation cohort. A classification decision tree was subsequently built for the diagnosis. Our findings further the understanding of the underlying molecular mechanisms in IU and VKH and facilitate the development of potential therapeutic and diagnostic strategies.

Enhancing Reliability of Advanced Driver-Assistance Systems through Predictive Maintenance and Data-Driven Insights

The advancement of Advanced Driver Assistance Systems (ADAS) marks a pivotal evolution in automotive technology, aiming to enhance road safety and driving efficiency through a wide array of functionalities like blind spot detection, emergency braking, and adaptive cruise control. This research paper delves into the operational integrity, performance metrics, and maintenance strategies of ADAS components, underpinned by a comprehensive methodology involving data collection, pre-processing, feature engineering, machine learning model development, and rigorous validation processes. Systematic inspection of ADAS components indicates their importance in vehicle safety and reliability. The visibility, distance, speed, and steering angle of front cameras, LiDAR, radar, and ultrasonic sensors are carefully evaluated. Maintenance logs show proactive error code management, boosting efficiency. SVM, Gradient Boosting, and Random Forest machine learning models predicted ADAS component failures during validation and testing. Random Forest scored 90% accuracy, 92% precision, 88% recall, and 90% F1. Gradient Boosting was the most accurate, with 93% accuracy, 94% precision, 91% recall, and 92% F1. SVM predicted ADAS component failures with 88% accuracy, 90% precision, 85% recall, and 87% F1 score. Machine learning helps shift from reactive to proactive maintenance. Modelling sensor signal quality, actuator reaction times, error code frequencies, and maintenance intervals enables predictive maintenance and failure detection. Feature engineering builds predictive models using maintenance logs and operational KPIs. The models predict ADAS component failures, boosting vehicle safety and dependability. Using external data improves predictive maintenance models. The maintenance model's adaptability and forecast accuracy are proved by ADAS operation after traffic, accident, and manufacturer upgrades. Predictive maintenance and machine learning improve ADAS dependability and safety, the study found. Advanced analytics and data-driven insights can reduce automotive system failures, improving safety and reliability. 

Investigation of effect of processing parameters of 3D printed NHS/EDC crosslinked carboxy methyl cellulose/gelatin hydrogels with machine learning techniques

This study focuses on the effects of preparing conditions of gelatin/carboxymethyl cellulose (CMC) composites on their mechanical properties of gelatin/carboxymethyl cellulose (CMC) by extreme gradient boosting (XGB) machine learning algorithm. The research involved studying the effect of weight fraction of carboxymethyl cellulose (CMC) and graphene oxide (GO) as well as the concentration of ethyl(dimethylaminopropyl)carbodiimide (EDC)/ N-hydroxysuccinimide (NHS) on modulus, % strain at break and ultimate tensile strength (UTS). It also includes a correlation heatmap, feature importance assessment, model performance evaluation, and the Shapley Additive Explanation (SHAP) technique to analyze the dataset. The relationship between independent parameters and mechanical properties reveals insights into the material’s ductility, flexibility, and modulus. Feature importance demonstrates that NHS/EDC concentration has the highest impact on the mechanical properties. Increase of EDC/NHS concentration is observed to drastically elevate the modulus and UTS, however, reduces the flexibility of the nanocomposites. CMC improves flexibility but reduces UTS and modulus. GO improves % strain at break, UTS and modulus up to 1% GO, however, higher wt% of GO reduces the mechanical performance. With lower concentrations of NHS/EDC, the mechanical properties can be tailored for soft tissue engineering applications. The study highlights the importance of optimizing material compositions for tissue engineering applications.

Explainable artificial intelligence modeling of internal arc in a medium voltage switchgear based on different CFD simulations

The internal arc represents an unintentional release of electrical energy within the switchgear industry. Manufacturers must address this electro-thermal issue in their switchgears. Over the past decades, various researchers and engineering groups have examined the internal arc pressure rise in switchgears to mitigate damages. The high variability in pressure rise among switchgears due to diverse factors such as design, manufacturing, and electrical parameters results in varying reported pressure increases. This issue motivates the application of artificial intelligence (AI) in interpreting internal arc modeling. The present paper explores the impact of manufacturing parameters such as total duct width (TDW), height (H), and ducts condition (DC), along with environmental parameters like initial pressure (IP) and initial temperature (IT), on the maximum pressure (MP) generated during an internal arc in a medium voltage (MV) switchgear. For this purpose, 54 different computational fluid dynamics (CFD) models were built using the parameters indicated. An extreme gradient boosting (XGBoost) machine learning (ML) model was trained using different CFD models, with MP serving as the target variable for the ML model. The obtained results reveal a variation in the MP of the internal arc under the mentioned parameters, ranging from 17835.45 Pa to 144423.2 Pa. Using SHAP data revealed that IP, TDW, and DC were the most significant factors affecting the pressure increase of the internal arc phenomena.

Forecasting Sustainable Water Production in Convex Tubular Solar Stills using Gradient Boosting Analysis

Water scarcity is an important global issue that necessitates the development of sufficient and sustainable desalination technologies. This study forecasts the productivity of two solar distillation technologies, namely the conventional tubular solar still (TSS) and the convex tubular solar still (CTSS). The research objectives included assessing the distillate yield of both solar stills and investigating the application of an advanced gradient boosting machine learning (ML) technique for forecasting distillate production. Compared to the TSS, the CTSS demonstrated a calculated increase in productivity which indicates its potential as an effective water desalination technology. The correlation analysis revealed that the TSS exhibited 10 significant correlations while the CTSS exhibited 4 correlations. The application of the gradient boosting model revealed exceptional predictive precision for both solar stills. R-squared (R2) for the TSS model was 0.86, the Root Mean Squared Error (RMSE) was 58.2%, and the Coefficient of Variation of Root Mean Squared Error (CVRMSE) was 29.3%. In contrast, the CTSS model displayed impressive performance metrics, including an R2 value of 0.99, an RMSE value of 1.2%, and a CVRMSE value of 4%. Valuable insights were provided for the enhancement of solar stills, in addition to highlighting advanced ML techniques for accurately predicting productivity.

Gradient Boosting for Predicting the Relation Between Bio-medical Signals and Seizures Using LGBM and XGBoost

Background and aim: In recent years, research in the fields of brain-computer interfacing techniques and related areas are developing at a very rapid rate with the help of exploding of Artificial Intelligence, Machine Learning and Deep Learning. A new concept of Gradient Boosting has become popular research area among the researchers related to the field of automatic classification of Electroencephalograph (EEG) signals for predication of mental health issues like seizures. Methods: However effective feature extraction from EEG and accurately classify them with efficient classifiers is still an important task and attracted wide attention in this area. Therefore in this paper, we presented the detailed mathematical analysis of these methods and ensemble learnings based EEG signals classification method for seizures classification in EEG using Extreme Gradient Boosting Model such as Light Gradient Boosting Machine Learning (LGBM) and XGBoost. Results: Time-frequency domain based non-linear features are selected from preprocessed EEG Dataset, and PCA (Principal Component Analysis) is used for dimensionality reduction for features engineering, then optimized feature based training and testing is done for two class classification in ensemble learning method i.e. LGBM and XGBoost. Finally, both models are tested with dataset of University of Bonn, Germany to classify the signals. Conclusions: In addition this paper highlights the Correlation Analysis Methodology to Identify Strong Predictor and Attributes Correlation-based Attribute Ranking for the Feature Engineering which has proved to be more efficient in EEG signals Classification and provide comparative analysis with other existing models for performance evaluation in terms of accuracy which is 87.34 and 92.31 for LBGM and XGBoost, sensitivity of 85.21 and 90.18 and specificity of 83.0 and 90.04 for LBGM and XGBoost.

Large-scale plasma proteomics in the UK Biobank modestly improves prediction of major cardiovascular events in a population without previous cardiovascular disease.

Improved identification of individuals at high risk of developing cardiovascular disease would enable targeted interventions and potentially lead to reductions in mortality and morbidity. Our aim was to determine whether use of large-scale proteomics improves prediction of cardiovascular events beyond traditional risk factors (TRFs). Using proximity extension assays, 2919 plasma proteins were measured in 38 380 participants of the UK Biobank. Both data- and literature-based feature selection and trained models using extreme gradient boosting machine learning were used to predict risk of major cardiovascular events (MACE: fatal and non-fatal myocardial infarction, stroke and coronary artery revascularisation) during a 10-year follow-up. Area under the curve (AUC) and net reclassification index (NRI) were used to evaluate the additive value of selected protein panels to MACE prediction by Systematic COronary Risk Evaluation 2 (SCORE2) or the 10 TRFs used in SCORE2. SCORE2 and SCORE2 refitted to UK Biobank data predicted MACE with AUCs of 0.740 and 0.749, respectively. data-driven selection identified 114 proteins of greatest relevance for prediction. Prediction of MACE was not improved by using these proteins alone (AUC of 0.758) but was significantly improved by combining these proteins with SCORE2 or the 10 TRFs (AUC=0.771, p<001, NRI=0.140, and AUC=0.767, p=0.03, NRI 0.053, respectively). Literature-based protein selection (113 proteins from five previous studies) also improved risk prediction beyond TRFs while a random selection of 114 proteins did not. Large-scale plasma proteomics with data-driven and literature-based protein selection modestly improves prediction of future MACE beyond TRFs.

A Novel Approach to Detection of Alzheimer’s Disease from Handwriting: Triple Ensemble Learning Model

The irreversible degeneration of nerve cells in the body dramatically affects the motor skills and cognitive abilities used effectively in daily life. There is no known cure for neurodegenerative diseases such as Alzheimer’s. However, in the early diagnosis of such diseases, the progression of the disease can be slowed down with specific rehabilitation techniques and medications. Therefore, early diagnosis of the disease is essential in slowing down the disease and improving patients’ quality of life. Neurodegenerative diseases also affect patients’ ability to use fine motor skills. Losing fine motor skills causes patients’ writing skills to deteriorate gradually. Information about Alzheimer’s disease can be obtained based on the deterioration in the patient’s writing skills. However, manual detection of Alzheimer’s disease (AD) from handwriting is a time-consuming and challenging task that varies from physician to physician. Machine learning-based classifiers are extremely popularly used with high-performance scores to solve the challenging manual detection of AD. In this study, Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) machine learning classification algorithms were combined with a Voting Classifier and trained and tested on the publicly available DARWIN (Diagnosis Alzheimer’s With haNdwriting) dataset. As a result of the experimental studies, the proposed Ensemble methodology achieved 97.14% Acc, 95% Prec, 100% Recall, 90.25% Spec, and 97.44% F1-score (Dice) performance values. Studies have shown that the proposed work is exceptionally robust.

Interpretable machine learning-based individual analysis of acute kidney injury in immune checkpoint inhibitor therapy.

Acute kidney injury (AKI) is a critical complication of immune checkpoint inhibitor therapy. Since the etiology of AKI in patients undergoing cancer therapy varies, clarifying underlying causes in individual cases is critical for optimal cancer treatment. Although it is essential to individually analyze immune checkpoint inhibitor-treated patients for underlying pathologies for each AKI episode, these analyses have not been realized. Herein, we aimed to individually clarify the underlying causes of AKI in immune checkpoint inhibitor-treated patients using a new clustering approach with Shapley Additive exPlanations (SHAP). We developed a gradient-boosting decision tree-based machine learning model continuously predicting AKI within 7 days, using the medical records of 616 immune checkpoint inhibitor-treated patients. The temporal changes in individual predictive reasoning in AKI prediction models represented the key features contributing to each AKI prediction and clustered AKI patients based on the features with high predictive contribution quantified in time series by SHAP. We searched for common clinical backgrounds of AKI patients in each cluster, compared with annotation by three nephrologists. One hundred and twelve patients (18.2%) had at least one AKI episode. They were clustered per the key feature, and their SHAP value patterns, and the nephrologists assessed the clusters' clinical relevance. Receiver operating characteristic analysis revealed that the area under the curve was 0.880. Patients with AKI were categorized into four clusters with significant prognostic differences (p = 0.010). The leading causes of AKI for each cluster, such as hypovolemia, drug-related, and cancer cachexia, were all clinically interpretable, which conventional approaches cannot obtain. Our results suggest that the clustering method of individual predictive reasoning in machine learning models can be applied to infer clinically critical factors for developing each episode of AKI among patients with multiple AKI risk factors, such as immune checkpoint inhibitor-treated patients.

Development of a Machine Learning Model to Predict the Use of Surgery in Patients With Rheumatoid Arthritis.

One in five patients with rheumatoid arthritis (RA) rely on surgery to restore joint function. However, variable response to disease-modifying antirheumatic drugs (DMARDs) complicates surgical planning, and it is difficult to predict which patients may ultimately require surgery. We used machine learning to develop predictive models for the likelihood of undergoing an operation related to RA and which type of operation patients who require surgery undergo. We used electronic health record data to train two extreme gradient boosting machine learning models. The first model predicted patients' probabilities of undergoing surgery ≥5 years after their initial clinic visit. The second model predicted whether patients who underwent surgery would undergo a major joint replacement versus a less intensive procedure. Predictors included demographics, comorbidities, and medication data. The primary outcome was model discrimination, measured by area under the receiver operating characteristic curve (AUC). We identified 5,481 patients, of whom 278 (5.1%) underwent surgery. There was no significant difference in the frequency of DMARD or steroid prescriptions between patients who did and did not have surgery, though nonsteroidal anti-inflammatory drug prescriptions were more common among patients who did have surgery (P = 0.03). The model predicting use of surgery had an AUC of 0.90 ± 0.02. The model predicting type of surgery had an AUC of 0.58 ± 0.10. Predictive models using clinical data have the potential to facilitate identification of patients who may undergo rheumatoid-related surgery, but not what type of procedure they will need. Integrating similar models into practice has the potential to improve surgical planning.

Tree-Based Machine Learning to Identify Predictors of Psoriasis Incidence at the Neighborhood Level: A Populational Study from Quebec, Canada.

Psoriasis is a major global health burden affecting ~ 60 million people worldwide. Existing studies on psoriasis focused on individual-level health behaviors (e.g. diet, alcohol consumption, smoking, exercise) and characteristics as drivers of psoriasis risk. However, it is increasingly recognized that health behavior arises in the context of larger social, cultural, economic and environmental determinants of health. We aimed to identify the top risk factors that significantly impact the incidence of psoriasis at the neighborhood level using populational data from the province of Quebec (Canada) and advanced tree-based machine learning (ML) techniques. Adult psoriasis patients were identified using International Classification of Disease (ICD)-9/10 codes from Quebec (Canada) populational databases for years 1997-2015. Data on environmental and socioeconomic factors 1year prior to psoriasis onset were obtained from the Canadian Urban Environment Health Consortium (CANUE) and Statistics Canada (StatCan) and were input as predictors into the gradient boosting ML. Model performance was evaluated using the area under the curve (AUC). Parsimonious models and partial dependence plots were determined to assess directionality of the relationship. The incidence of psoriasis varied geographically from 1.6 to 325.6/100,000 person-years in Quebec. The parsimonious model (top 9 predictors) had an AUC of 0.77 to predict high psoriasis incidence. Amongst top predictors, ultraviolet (UV) radiation, maximum daily temperature, proportion of females, soil moisture, urbanization, and distance to expressways had a negative association with psoriasis incidence. Nighttime light brightness had a positive association, whereas social and material deprivation indices suggested a higher psoriasis incidence in the middle socioeconomic class neighborhoods. This is the first study to highlight highly variable psoriasis incidence rates on a jurisdictional level and suggests that living environment, notably climate, vegetation, urbanization and neighborhood socioeconomic characteristics may have an association with psoriasis incidence.

A platform-independent AI tumor lineage and site (ATLAS) classifier

Histopathologic diagnosis and classification of cancer plays a critical role in guiding treatment. Advances in next-generation sequencing have ushered in new complementary molecular frameworks. However, existing approaches do not independently assess both site-of-origin (e.g. prostate) and lineage (e.g. adenocarcinoma) and have minimal validation in metastatic disease, where classification is more difficult. Utilizing gradient-boosted machine learning, we developed ATLAS, a pair of separate AI Tumor Lineage and Site-of-origin models from RNA expression data on 8249 tumor samples. We assessed performance independently in 10,376 total tumor samples, including 1490 metastatic samples, achieving an accuracy of 91.4% for cancer site-of-origin and 97.1% for cancer lineage. High confidence predictions (encompassing the majority of cases) were accurate 98–99% of the time in both localized and remarkably even in metastatic samples. We also identified emergent properties of our lineage scores for tumor types on which the model was never trained (zero-shot learning). Adenocarcinoma/sarcoma lineage scores differentiated epithelioid from biphasic/sarcomatoid mesothelioma. Also, predicted lineage de-differentiation identified neuroendocrine/small cell tumors and was associated with poor outcomes across tumor types. Our platform-independent single-sample approach can be easily translated to existing RNA-seq platforms. ATLAS can complement and guide traditional histopathologic assessment in challenging situations and tumors of unknown primary.

The Impacts of Open Data and eXplainable AI on Real Estate Price Predictions in Smart Cities

In the rapidly evolving landscape of urban development, where smart cities increasingly rely on artificial intelligence (AI) solutions to address complex challenges, using AI to accurately predict real estate prices becomes a multifaceted and crucial task integral to urban planning and economic development. This paper delves into this endeavor, highlighting the transformative impact of specifically chosen contextual open data and recent advances in eXplainable AI (XAI) to improve the accuracy and transparency of real estate price predictions within smart cities. Focusing on Lisbon’s dynamic housing market from 2018 to 2021, we integrate diverse open data sources into an eXtreme Gradient Boosting (XGBoost) machine learning model optimized with the Optuna hyperparameter framework to enhance its predictive precision. Our initial model achieved a Mean Absolute Error (MAE) of EUR 51,733.88, which was significantly reduced by 8.24% upon incorporating open data features. This substantial improvement underscores open data’s potential to boost real estate price predictions. Additionally, we employed SHapley Additive exPlanations (SHAP) to address the transparency of our model. This approach clarifies the influence of each predictor on price estimates and fosters enhanced accountability and trust in AI-driven real estate analytics. The findings of this study emphasize the role of XAI and the value of open data in enhancing the transparency and efficacy of AI-driven urban development, explicitly demonstrating how they contribute to more accurate and insightful real estate analytics, thereby informing and improving policy decisions for the sustainable development of smart cities.

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

Materials used in aircraft engines, gas turbines, nuclear reactors, re-entry vehicles, and hypersonic structures are subject to severe environmental conditions that present significant challenges. With their remarkable properties, such as high melting temperatures, strong resistance to oxidation, corrosion, and ablation, minimal creep, and advantageous thermal cycling behavior, ceramic matrix composites (CMCs) show great promise as a material to meet the strict requirements in these kinds of environments. Furthermore, the addition of boron nitride nanoparticles with continuous fibers to the CMCs can offer thermal resistivity in harsh conditions, which will improve the composites’ strength and fracture toughness. Therefore, in extreme situations, it is crucial to understand the thermal resistivity period of composite materials. To forecast the ablation performance of composites, we developed six machine learning regression methods in this study: decision tree, random forest, support vector machine, gradient boosting, extreme gradient boosting, and adaptive boosting. When evaluating model performance using metrics including R2 score, root mean square error, mean absolute error, and mean absolute percentage error, the gradient boosting and extreme gradient boosting machine learning regression models performed better than the others. The effectiveness of machine learning models as a useful tool for forecasting the ablation behavior of ceramic matrix composites was effectively explained by this study.

Measurement and analysis of void fraction in horizontal plug flow based on electromagnetic wave sensor

Void fraction measurement is a crucial challenge in two-phase flow, which is related to the measurement of phase flow and the calculation of pressure drop. In this study, a coaxial line phase sensor is designed for void fraction acquisition and the void fraction of the plug measured has a well time-domain and Probability Density Functions (PDF) distribution characteristics. Four typical correlations were selected to compare with the measured values of the sensors, the mean absolute errors (MAPE) are 19.88 %, 18.94 %, 18.25 %, and 17.19 %, respectively. Considering the nonlinear and strong coupling between the flow characteristic parameters and the void fraction, the flow parameters were analyzed and selected by Pearson correlation coefficient. The Kernel ridge regression methods were employed to select effective variables for model training, and the Extreme gradient boosting (XGboost) machine learning model was proposed. The prediction accuracy of the model is 1.65 % (current data) and 4.18 % (literature data), respectively.

Enhancing flow stress predictions in CoCrFeNiV high entropy alloy with conventional and machine learning techniques

A machine learning technique leveraging artificial intelligence (AI) has emerged as a promising tool for expediting the exploration and design of novel high entropy alloys (HEAs) while predicting their mechanical properties at both room and elevated temperatures. In this paper, we predict the flow stress of hot-compressed CoCrFeNiV HEAs using conventional (qualitative and quantitative models) and advanced machine learning approaches across various temperature and strain rate conditions. Conventional modeling methods, including the modified Johnson-Cook (JC), modified Zerilli–Armstrong (ZA), and Arrhenius-type constitutive equations, are employed. Simultaneously, machine learning models are utilized to forecast flow stress under different hot working conditions. The performance of both conventional and machine learning models is evaluated using metrics such as coefficient of determination (R2), mean abosolute error (MAE), and root mean squared error (RMSE). The analysis reveals that the gradient boosting machine learning model shows superior prediction accuracy (with value R2 = 0.994, MAE = 7.77%, and RMSE = 9.7%) compared to conventional models and other machine learning approaches.