Gradient Boosting Research Articles

Innovative machine learning approaches for prediction of hypoglycemia in patients with type 2 Diabetes

Medical data science advances using machine learning, which predicts glucose levels. A supervised machine learning technique is employed in which regression and classification methods are used to check the prediction performance. The unsupervised machine learning technique makes clusters based on variables' similarities. Furthermore, the prediction accuracy of conventional machine learning techniques is improved by proposing a transfer learning technique. Based on a median value of 67 mg/dL, the data set is divided into two groups: group 1 (BSL 57 mg/dL to 67 mg/dL) has 50.67% of the samples, and group 2 (with BSL 68 mg/dL to 79 mg/dL) has 49.33% of the samples. In regression analysis, 5-fold cross-validation is performed. The decision tree (DT) and gradient boosting (GB) individually provide a prediction accuracy of 18.2%. Regarding classification analysis, a 10-fold cross-validation configuration is used for training and testing the model. AdaBoost, GB, random forest, and neural network achieve an accuracy rate of 66.3% and an area under curve (AUC) score of 0.731. In unsupervised learning, the datasets are divided into three clusters. The clustering result is used in regression and classification models using transfer learning. The accuracy and precision of the AdaBoost and GB are as follows: 69.6%, 0.696 with f1 0.661 and 69.6%, 0.708 with f1 0.708, respectively.

Optimizing IoT-driven smart grid stability prediction with dipper throated optimization algorithm for gradient boosting hyperparameters

With the surge in global population and economic expansion, there's been a marked increase in electricity demand. This necessitates the efficient distribution of electricity to both residential and industrial sectors to minimize energy loss. Smart Grids (SG) emerge as a promising solution to reduce power dissipation in distribution networks. The application of machine learning and artificial intelligence in SGs has significantly improved the precision of predicting consumer electricity needs. This paper presents a novel approach to improving the stability prediction of Internet of Things (IOT)-driven SGs using different advanced machine learning models. This study explores multiple advanced machine-learning techniques, including Gradient Boosting (GB), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Neural Networks, and the Decision Tree classifier, focusing on the stability prediction of SGs. This study explores the efficiency of hyperparameter-optimized GB models in predicting SG dynamic stability that encompasses the ability of the system to return to a stable operating point following a disturbance. Focusing on various models, it identifies the Dipper Throated Optimization Algorithm DTO+GB model as the standout, exhibiting unparalleled accuracy and reliability across critical performance metrics such as accuracy (99.32 %), sensitivity (99.16 %), and specificity (99.54 %). Diagnostic and regression analyses further emphasize its better predictive power and the need for hyperparameter optimization to improve the model. This paper highlights the capabilities of advanced machine learning algorithms in conjunction with tactical hyperparameter optimization in enhancing SG stability prediction, introducing a new baseline for future technological and methodological developments in this application.

Predicting hepatitis C infection with machine learning algorithms: a prospective study

<p>Globally, chronic hepatitis C virus (HCV) infection affects millions of people and leads to a high number of deaths annually. In 2019, the World Health Organization (WHO) recorded around 290,000 deaths related to HCV, a virus transmitted mainly through blood that causes liver damage. The virus has infected more than 169 million people worldwide. This study aims to compare the performance of machine learning (ML) models for HCV detection. ML models such as logistic regression (LR), random forest (RF), decision tree (DT), and catBoost classifier (CATBC) were used. To carry out this task, a dataset of 615 patient records, and 14 variables were used. This research process was carried out in multiple phases, encompassing model understanding, data analysis and cleaning, ML model training, and subsequent model evaluation. The results revealed that the gradient boosting (GB) model stood out by achieving the best performance and highest accuracy, achieving a rate of 94% in HCV detection, this demonstrates outstanding performance compared to the other models such as LR, RF, k-nearest neighbor (KNN), DT, and CATBC, which obtained accuracy rates of 89%, 93%, 85%, 93%, 93%, and 92%, respectively. It can be concluded that the GB model stands out as the best algorithm for this task.</p>

Reliability of Regression Based Hybrid Machine Learning Models for the Prediction of Solar Photovoltaics Power Generation

Solar Photovoltaics (PVs) have become widespread as micro-distributed electric power generators in urban residential and commercial areas due to their affordability and minimal maintenance requirements. Despite these advantages, PV generation is intermittent, necessitating the implementation of robust predictive algorithms to capture power generation trends effectively. Accurate PV generation prediction is crucial for balancing energy demand and supply. Precise predictions help utilities use other grid resources efficiently, enhance market participation, and make informed planning decisions. Therefore, ensuring the precise performance of predictive models is imperative in the power generation industry. However, the performance of the predictive models typically fluctuates over time, necessitating a reliability assessment of their performance. This paper develops a hybrid machine learning model by integrating the support vector machine (SVM), random forest (RF), and gradient boosting machine (GBM) with multivariate adaptive regression spline (MARS) to predict the hourly PV power generation for 3-day and 7-day periods in three urban areas of North Dakota. The performance of the models has been recorded over a year, and a probability distribution model is proposed to demonstrate the hybrid algorithm’s reliability and effectiveness. Results show the proposed prediction model is more reliable for shorter prediction periods. The hybrid-MARS models significantly improve prediction reliability compared to the stand-alone MARS model. Among these models, MARS-SVM exhibits the highest reliability and consistently better accuracy than other hybrid models.

Deciphering the nonlinear and synergistic role of building energy variables in shaping carbon emissions: A LightGBM- SHAP framework in office buildings

As the global demand for sustainable buildings continues to rise, accurately assessing and managing carbon emissions during the operational phase of buildings has become an urgent challenge. This study employs machine learning and Explainable Artificial Intelligence (XAI) to explore the impact and synergistic effects of energy variables on carbon emissions in office building operations. Initially, energy consumption data from 28 office buildings in China were collected to construct a dataset comprising 24 energy indicators across four dimensions. The Lightweight Gradient Boosting Machine (LightGBM) and Extreme Gradient Boosting (XGBoost) models were used to predict building carbon emission, PV carbon offset, and net carbon emission based on different energy parameters. The SHapley Additive exPlanations (SHAP) method was utilized to interpret the model results from both global and local perspectives. The study results indicate that: (1) The LightGBM model, optimized with GridSearchCV, shows better stability and reliability in predicting carbon emissions. (2) SHAP bar plots和SHAP beeswarm plots reveal that the WWR in the ES dimension and the PVArea in the EP dimension are the most critical variables affecting building carbon emission and PV carbon offset. (3) SHAP dependence plots show that the contributions of energy variables are not independent, with differentiated synergistic effects among variables in emission reduction. (4) SHAP force plots consider interactions among multiple variables, identifying key variable ranges to minimize building carbon emission and maximize PV carbon offset.

Data-driven model assessment: A comparative study for ship response determination

Several machine learning approaches to determine ship responses via data-driven models have been applied. Input features and parameters used relied on time-series analyses obtained from computational-fluid-dynamics approach. As inputs into the data-driven model, the heave and pitch motions of a ship advancing in irregular, i.e., natural seaway were considered. By using different models in the framework of machine learning, required computation for the associated ship motions may be avoided, thus reducing the computational effort to forecast ship motions. Comparative predictions with numerical simulations revealed that the deep-neural-network method for training in auto-machine-learning instructions yielded the highest accuracy in heave motion, resulting in a non-normalized mean-absolute-error of 0.74, against the corresponding error of 1.07 from numerical computations, whereas the method trained with the tree-based models (the extreme gradient boosting and the Hist gradient boosting regressor) predicted less accurate motions for the tested ship. The model trained with the random forest regressor exhibited an error of 1.10. Numerical simulation based on a field method proved to be the most suitable choice for pitch motion. Despite the few samples available to train the regressors, results demonstrated that the measured data was sufficient to assess the developed data-driven model for ship response determination.

Computational Intelligence for Modeling the Rheological Properties of the Developed Hydrated Lime-Based Alkali-Activated Materials

The development of alkali-activated materials (AAMs) has gained attention as a sustainable alternative to Portland cement-based concrete. A key factor in AAM performance is its rheological behavior, which influences workability and pumpability. Accurate prediction of these properties is vital for optimizing mix design. This study introduces a computational framework using ensemble and non-ensemble machine learning (ML) techniques to model the yield stress (YS) and plastic viscosity (PV) of AAMs. A comprehensive dataset from previous experiments was used to train models, including artificial neural networks (ANN), gene expression programming (GEP), decision tree, random forest, adaptive boosting, and gradient boosting. The models achieved R² values exceeding 0.95 and correlation coefficients above 0.97. Error metrics, such as MAPE, MAE, MSE, and RMSE, further validated the models’ generalizability. A parametric analysis showed that a 20% increase in hydrated lime (HL) content raised YS by 350Pa, which is similar to the effect of varying fly ash (FA) from 60% to 100%. A 40% increase in FA and HL content increased PV by 4Pa.s and 25Pa.s, respectively. A 3% rise in sodium silicate led to a 400Pa increase in YS, while a similar increase in sodium hydroxide raised YS by 170Pa and PV by 15Pa·s. Overall, this study provides an effective tool for optimizing AAM mix designs by accurately predicting rheological properties and quantifying the impact of key parameters, contributing to the advancement of sustainable cementitious materials in construction.

Enhancing breast cancer diagnosis: a comparative analysis of feature selection techniques

Breast cancer is a significant contributor to female mortality, emphasizing the importance of early detection. Predicting breast cancer accurately remains a complex challenge within medical data analysis. Machine learning (ML) algorithms offer valuable assistance in decision-making and diagnosis using medical data. Numerous research studies highlight the effectiveness of ML techniques in improving breast cancer prediction. Feature selection plays a pivotal role in data preprocessing, eliminating irrelevant and redundant features to minimize feature count and improve classification accuracy. This study focuses on optimizing breast cancer diagnostics through feature selection methods, specifically genetic algorithms (GA) and particle swarm optimization (PSO). The research involves a comparative analysis of these methods and the application of a diverse set of ML classification techniques, including logistic regression (LR), support vector machine (SVM), decision tree (DT), and ensemble methods like random forest (RF), AdaBoost, and gradient boosting (GB), using a breast cancer dataset. The models' performance is subsequently evaluated using various performance metrics. The experimental findings illustrate that PSO achieved the highest average accuracy, reaching 99.6% when applied to AdaBoost, while GA attained an accuracy rate of 99.5% when employed with both AdaBoost and RF.

Advancing elderly care through big data analytics and machine learning for daily activity characterization

<p>Confronted with the ongoing demographic shift characterized by an aging population, society grapples with emerging challenges that extend beyond the provision of targeted health services for the elderly. The focus has broadened to encompass the promotion of well-being and vitality throughout the aging process. Addressing these multifaceted issues demands a comprehensive approach that integrates biomedical components with physical, psychological, and social interventions. In the context of my project, a unique strategy is employed, placing significant emphasis on leveraging big data analytics and machine learning. The primary objective is to systematically observe and characterize the physiological conditions of the elderly, facilitating healthcare professionals in monitoring behaviors and promoting active aging. This undertaking involves meticulous data collection and analysis, employing machine learning algorithms (support vector machine (SVM), gradient boosting) within a framework that harnesses extensive data analytics. Ultimately, this approach enables the identification and characterization of daily routines and physiological states of individuals, contributing to a holistic understanding of aging.</p>

Predictive modeling and anomaly detection in large-scale web portals through the CAWAL framework

This study presents an approach that uses session and page view data collected through the CAWAL framework, enriched through specialized processes, for advanced predictive modeling and anomaly detection in web usage mining (WUM) applications. Traditional WUM methods often rely on web server logs, which limit data diversity and quality. Integrating application logs with web analytics, the CAWAL framework creates comprehensive session and page view datasets, providing a more detailed view of user interactions and effectively addressing these limitations. This integration enhances data diversity and quality while eliminating the preprocessing stage required in conventional WUM, leading to greater process efficiency. The enriched datasets, created by cross-integrating session and page view data, were applied to advanced machine learning models, such as Gradient Boosting and Random Forest, which are known for their effectiveness in capturing complex patterns and modeling non-linear relationships. These models achieved over 92% accuracy in predicting user behavior and significantly improved anomaly detection capabilities. The results show that this approach offers detailed insights into user behavior and system performance metrics, making it a reliable solution for improving large-scale web portals’ efficiency, reliability, and scalability.

Novel maternal risk factors for preeclampsia prediction using machine learning algorithms

<div align="center"><table width="590" border="1" cellspacing="0" cellpadding="0"><tbody><tr><td valign="top" width="387"><p>Preeclampsia and eclampsia are the most common obstetric disorders associated with poor outcomes for women, fetuses, and newborns throughout the perinatal period. The study’s primary objective was to assess the accuracy of novel high-risk factors using MLA in predicting preeclampsia. The study included 400 pregnant women and used 27 novel high-risk factors to predict preeclampsia. The target variables for predicting preeclampsia are systolic and diastolic blood pressures. Various algorithms, including DT, RF, Gradient Boosting, SVM, K-Neighbors, LGBM, MLP, Ada Boost Classifier, and Extra Trees Classifier are used in the analysis. The accuracy and precision of the LGBM Classifier (0.85 and 0.9583 with F1 0.7188), Support Vector Classifier (0.8417 and 0.92 with F1 0.7077), Decision Tree (0.825 and 0.913 with F1 0.6667), and Extra Trees (0.8167 and 0.9091 with F1 0.6452) are found to be better algorithms for prediction of preeclampsia than those of the other models used in the study. According to the novel high-risk factors score, 17.5% of pregnant women were identified as being at high risk for preeclampsia during the first trimester, which increased to 18.7% in 3rd trimester; in addition, 16% of pregnant women had a BP of 140/90 mmHg and the above. Novel, high-risk scores and MLA can effectively predict preeclampsia at an early period.</p></td></tr></tbody></table></div>

Cloud radon data for earthquake magnitude prediction using machine learning

<span>The study investigates the potential of integrating radon gas concentration telemonitoring systems with machine learning techniques to enhance earthquake magnitude prediction. Conducted in Pacitan, East Java, Indonesia, where the stations are near the active Grundulu fault, the research employs Random Forest (RF), Extreme Gradient Boosting (XGB), Neural Network (NN), AdaBoost (AB), and Support Vector Machine (SVM) methods. Utilizing real-time radon gas concentration measurements, the study aims to refine earthquake magnitude prediction, crucial for disaster preparedness. The evaluation involves multiple metrics like Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Mean Squared Error (MSE), Symmetric Mean Absolute Percentage Error (SMAPE), and cnSMAPE. XGB and SVM emerge as top performers, showcasing superior predictive accuracy with minimal errors across various metrics. XGB achieved MAE (0.33), MAPE (6.03%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97), while SVM recorded MAE (0.34), MAPE (6.20%), RMSE (0.51), MSE (0.26), SMAPE (0.06), and cnMAPE (0.97). The analysis reveals XGB as the most effective method, boasting the lowest error values. The study underscores the importance of expanding data availability to enhance predictive models, ultimately contributing to more precise earthquake magnitude predictions and effective mitigation strategies.</span>

Anomaly detection in bridge structural health monitoring via 1D-LBP and statistical feature fusion

Over the past few years, numerous bridges have been equipped with structural health monitoring (SHM) systems to continuously monitor critical structural parameters, enabling early detection of potential issues and timely maintenance. However, the monitoring data frequently contain anomalies due to various interferences during the acquisition and transmission processes. To address this, this paper proposes a robust and efficient anomaly detection and classification method. The method extracts one-dimensional local binary pattern (1D-LBP) features and time-domain statistical features from the monitoring data, fusing them into a comprehensive feature representation. These fused features are then input into an extreme gradient boosting (XG-Boost) classifier for anomaly detection. Additionally, a 1D-LBP feature simplification method is introduced to enhance detection efficiency. The effectiveness of the proposed method was validated using monitoring data from a long-span cable-stayed bridge SHM system. Experimental results demonstrate the excellent performance of the method in terms of detection accuracy and efficiency.

Machine learning (ML) algorithms for seismic vulnerability assessment of school buildings in high-intensity seismic zones

Ensuring seismic resilience of school buildings is crucial for safeguarding their occupants during earthquakes. This paper focuses on assessing the seismic vulnerability of school buildings constructed in the Kashmir region of Pakistan after the 2005 earthquake, which claimed the lives of 19,000 school-going children. It explores the feasibility of utilizing machine learning (ML) algorithms for enhanced rapid screening of schools to establish fragility information. The study is based on data collected in the Kashmir region and focuses on assessing representative reinforced concrete (RC) and unreinforced masonry (URM) school buildings. To determine structural fragility curves, Incremental Dynamic Analyses (IDA) are performed, simulating fifteen historical earthquakes. Four different ML models are investigated to predict fragility curves, including Random Forest (RF), Artificial Neural Networks (ANNs), Extreme Gradient Boosting (XGBoost), and Extremely Randomized Tree Regressor (ERTR). The performance of the algorithms is compared using performance metrics such as precision, accuracy, and f1 score. The study identified XGBoost and RF as the highest performing algorithms, achieving highly satisfactory accuracy with the correlation coefficients of 0.91 and 0.81 for RC schools, and 0.88 and 0.83 for URM schools during testing phases. Alternatively, ERTR’s performance could not justify its use for structural seismic vulnerability assessments. This highlights the significant potential of using ML algorithms for automated seismic vulnerability evaluation of buildings, greatly reducing the overall computational burden while maintaining high accuracy and reliability.

Advancing Mix Design Prediction in 3D Printed Concrete: Predicting Anisotropic Compressive Strength and Slump Flow

Introducing 3D-concrete printing has started a revolution in the construction industry, presenting unique opportunities alongside undeniable challenges. Among these, the major challenge is the iterative process associated with mix design formulation, which results in significant material and time consumption. This research uses machine learning (ML) techniques such as Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree Regression (DTR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) to overcome these challenges. A dataset containing 21 mix constituent features and 4 output properties (cast and printed compressive strength, and slump flow) was extracted from the literature to investigate the relationship between mix design and performance. The models were assessed using a range of evaluation metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared value. Gaussian Process Regression (GPR) yielded more favorable results. In the case of cast strength, GPR achieved an R2 value of 0.9069, along with RMSE, MSE, and MAE values of 13.04, 170.12, and 9.40, respectively. A similar trend was observed for printed strengths in directions 1, 2, and 3. GPR achieved R-squared values exceeding 0.91 for all directions, accompanied by significantly lower RMSE values (below 4.1). The machine learning models were also validated using four unique mix designs. These mixes were 3D printed and tested for compressive strength and slump flow. GPR's average error was 10.55%, while SVM achieved a slightly lower average error of 9.38%. Overall, this work presents a novel approach for optimizing 3D-printed concrete by enabling the prediction of slump flow and compressive strength directly from the mix design. This approach can facilitate the design and fabrication of 3D-printed concrete structures that fulfill the necessary strength and printability requirements.

Evidence from Machine Learning, Diagnostic Hub Genes in Sepsis and Diagnostic Models based on Xgboost Models, Novel Molecular Models for the Diagnosis of Sepsis.

Systemic multi-organ dysfunction resulting from dysregulated immune responses in the host triggered by microbial infection or other factors is a major cause of death in sepsis, and secretory pathways play an important role in it. GSE57065, GSE65682, GSE145227, and GSE54514 from Gene Expression Omnibus (GEO) were derived for this study. Secretory pathways single sample gene set enrichment analysis (ssGSEA) scores in sepsis and normal samples were exposed. Gene modules associated with secretory pathways were selected by weighted gene coexpression network analysis (WGCNA) for Protein-Protein Interaction Networks (PPI) assessment, and crossover genes in both were evaluated by eXtreme Gradient Boosting (XGBoost) model in feature selection to identify hub genes in sepsis. In addition, we explored the immune cells and signaling pathways regulated by hub genes. Remarkable dysregulation of secretory pathways was demonstrated in sepsis. The secretory pathways-associated gene modules were intimately involved in cytokine and immune responses in infection. Four crossover genes (CD163, FCER1G, C3AR1, ARG1) were present in WGCNA and PPI, and training in the XGBoost model revealed the best diagnostic performance of these 4 genes, meaning that these genes were the hub genes for sepsis. The 4-hub genes showed a significant negative correlation with T cell activity and a significant positive correlation with inflammatory immune cells. In addition, we found that the 4-hub genes markedly positively regulated INFLAMMATORY RESPONSE, IL6 JAK STAT3 SIGNALING. Based on WGCNA, PPI, and XGBoost models, we identified hub genes that play an important regulatory role in sepsis. We also developed novel molecular models for the diagnosis of sepsis.

Hybrid electrocoagulation/adsorption system using aluminum electrodes and novel GO@ZIF-7 nanocomposite for the effective removal of Pb(II) from wastewater

Water pollution resulting from heavy metals including lead [Pb(II)] is a major health concern for humans, animals, and aquatic life. Pb(II) is a highly hazardous contaminant that must be effectively removed from water before reuse or discharge. In this study, a hybrid electrocoagulation/adsorption system (EC/A) using aluminum electrodes integrated with a novel adsorbent GO@ZIF-7 nanocomposite, was studied for aqueous phase Pb(II) removal. The hybrid EC/A system yielded a near complete Pb(II) removal. The system was optimized using the Response Surface Methodology (RSM) based design of experiments (DOE) technique; specifically using the central composite design (CCD) the study analyzed the impacts of four primary process variables (i.e., current density, Pb(II) initial concentration, dosage of GO@ZIF-7 nanocomposite, and conductivity) on the Pb(II) removal. Notably, the findings show the significant role of current density in the Pb(II) removal process, particularly at a current density of 1.5 mA/cm2. The above-mentioned RSM design along with the analysis of variance (ANOVA) based statistical analysis and optimization, confirmed the suitability of the proposed RSM-based equations for Pb(II) removal modeling. Collectively, the findings of this study indicate that the proposed hybrid EC/A system using aluminum electrodes integrated with a novel GO@ZIF-7 nanocomposite has a significant practical viability as suggested by the remarkably high Pb(II) removal efficiency. Furthermore, this study also presents the effectiveness of three advanced machine learning (ML) based models, i.e., artificial neural network (ANN), eXtreme Gradient Boosting (XGBoost), and Random Forest Regression, for predictingthe Pb(II) removal using the EC/A process.

Elevating fraud detection: machine learning models with computational intelligence optimization

<span lang="EN-US">The amount of crimes committed online has undoubtedly increased as more people use the internet for e-commerce and other financial transactions. Machine learning algorithms have been created to detect payment fraud in online purchasing in order to address the issue. This study performs a thorough comparative examination of different metaheuristic optimizations as hyperparameter tuning methods; these are Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). They are used to optimize the ROC AUC of the three machine learning algorithms, namely X-Gradient Boost, Random Forest Classifier, and Light Gradient Boost Machine. Since the study's data are unbalanced, the determined metrics were ROC AUC. PSO offers consistent conditions for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations which are different from GA, a consistent condition for finding the best solution, according to our experiment. Without the inclusion of population annihilation strategies, PSO can achieve the greatest results in various situations. The findings indicate that Random Forest Classifier provided the highest ROC AUC value both before and after the hyperparameter tuning process, with a score of 88.69% attained while utilizing PSO.</span>

Somatic mutation of targeted sequencing identifies risk stratification in advanced ovarian clear cell carcinoma

BackgroundOvarian clear cell carcinoma (OCCC) is a unique subtype of epithelial ovarian cancer. Advanced OCCC display a poor prognosis. Therefore, we aimed to make risk stratification for precise medicine. MethodsWe performed a large next generation sequencing (NGS) gene panel on 44 patients with OCCC in FIGO stage II-IV. Then, by machine learning algorithms, including extreme gradient boosting (XGBoost), random survival forest (RSF), and Cox regression, we screened for feature genes associated with prognosis and constructed a 5-gene panel for risk stratification. The prediction efficacy of the 5-gene panel was compared with FIGO stage and residual disease by receiver operating characteristic curve and decision curve analysis. ResultsThe feature mutated genes related to prognosis, selected by machine learning algorithms, include MUC16, ATM, NOTCH3, KMT2A, and CTNNA1. The 5-gene panel can effectively distinguish the prognosis, as well as platinum response, of advanced OCCC in both internal and external cohorts, with the predictive capability superior to FIGO stage and residual disease. ConclusionsMutations in genes, including MUC16, ATM, NOTCH3, KMT2A, and CTNNA1, were associated with the poor prognosis of advanced OCCC. The risk stratification according to these genes demonstrated acceptable prediction power of prognosis and platinum response, suggesting the potential to be a novel target for precision medicine.

Comparative analysis of deep learning models for various nonalcoholic fatty liver disease datasets

Fatty liver disease is caused by increased liver buildup or weight above 5-10%. This disorder is widespread in people with diabetes, overweight persons, and metabolic syndrome patients. Clinical decision support systems can improve liver failure diagnosis and prediction to reduce this situation. Many liver failure models have drawbacks, and liver failure prediction is still a problem. This work uses four large open-access critical care patient datasets to create and verify liver failure risk prediction models. This study aims to construct a clinically applicable diagnostic and predictive model that evaluates the probability or risk of liver failure in intensive care unit (ICU) patients using extreme gradient boosting (XGBoost), artificial neural networks (ANN), multi-layer perceptron (MLP), Modular Neural Network (MNN), and generalized feed forward (GFF). We evaluated performance metrics using these models: accuracy, sensitivity, specificity, and predictive accuracy.