Hybrid Machine Learning and Deep Learning Models for Cardiovascular Disease Risk Prediction: A Comparative Analysis

Diagnosing a parasitic disease is a very difficult job in clinical practice. In this study, we constructed a machine learning model for diagnosis prediction using patient information. First, we diagnosed whether a patient has a parasitic disease. Next, we predicted the proper diagnosis method among the six types of diagnostic terms (biopsy, endoscopy, microscopy, molecular, radiology, and serology) if the patient has a parasitic disease. To make the datasets, we extracted patient information from PubMed abstracts from 1956 to 2019. We then used two datasets: the prediction for parasite-infected patient dataset (N = 8748) and the prediction for diagnosis method dataset (N = 3780). We then compared four machine learning models: support vector machine, random forest, multi-layered perceptron, and gradient boosting. To solve the data imbalance problem, the synthetic minority over-sampling technique and TomekLinks were used. In the parasite-infected patient dataset, the random forest, random forest with synthetic minority over-sampling technique, gradient boosting, gradient boosting with synthetic minority over-sampling technique, and gradient boosting with TomekLinks demonstrated the best performances (AUC: 79%). In predicting the diagnosis method dataset, gradient boosting with synthetic minority over-sampling technique was the best model (AUC: 87%). For the class prediction, gradient boosting demonstrated the best performances in biopsy (AUC: 88%). In endoscopy (AUC: 94%), molecular (AUC: 90%), and radiology (AUC: 88%), gradient boosting with synthetic minority over-sampling technique demonstrated the best performance. Random forest demonstrated the best performances in microscopy (AUC: 82%) and serology (AUC: 85%). We calculated feature importance using gradient boosting; age was the highest feature importance. In conclusion, this study demonstrated that gradient boosting with synthetic minority over-sampling technique can predict a parasitic disease and serve as a promising diagnosis tool for binary classification and multi-classification schemes.

This paper presents a data-driven approach to determine the load and flexural capacities of reinforced concrete (RC) beams strengthened with fabric reinforced cementitious matrix (FRCM) composites in flexure. A total of seven machine learning (ML) models such as kernel ridge regression, K-nearest neighbors, support vector regression, classification and regression trees, random forest, gradient boosted trees, and extreme gradient boosting (xgBoost) are evaluated to propose the best predictive model for FRCM-strengthened beams. Beam geometry, internal steel reinforcement area, FRCM reinforcement area, and mechanical characteristics of concrete, steel, and FRCM are the main input parameters included in the database. Among the studied ML models, the xgBoost model is the most accurate model with the highest coefficient of determination (R2=99.3%) and least root mean square (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). A comparative study of the performance of the proposed and existing analytical models revealed the superior predictive capability and robustness of the proposed model. The predicted flexural and load capacities of the beams based on the existing analytical models are highly scattered and either over-conservative or unsafe. A unified SHapley Additive exPlanations approach is employed to explain the output of the best ML model and identify the most significant input features and interactions that influence the capacity of FRCM-strengthened RC beams in flexure. Furthermore, a reliability analysis is performed to calibrate the resistance reduction factor (ϕ) to achieve a specified target reliability index (βT=3.5).

BackgroundAcute upper gastrointestinal bleeding (AUGIB) is one of the most common critical diseases encountered in the intensive care unit (ICU), with a mortality rate ranging from 15 to 20%. Accurate stratification of acute gastrointestinal bleeding into acute variceal gastrointestinal bleeding (AVGIB) and acute non-variceal gastrointestinal bleeding (ANGIB) subtypes is clinically essential as distinct entities require markedly different therapeutic approaches and even divergent prognostic implications. AUGIB characterized by hemorrhagic shock, hypotension, multiple organ dysfunction (MODS), and even circulatory failure is life-threatening. Machine learning (ML) prediction model can be an effective tool for mortality prediction, enabling the timely identification of high-risk patients and improving outcomes.MethodsA total of 3,050 acute upper gastrointestinal bleeding (AUGIB) patients were included in our research from the MIMIC-IV database, among which 625 patients were classified as AVGIB and 2,425 patients were categorized as ANGIB. Patients’ clinical features, intervention methods, vital signs, scores, and important laboratory results were collected. The Synthetic Minority Over-sampling Technique-Edited Nearest Neighbors (SMOTE-ENN) and Adaptive Synthetic Sampling (ADASYN) were adopted to address the imbalance of the dataset. As many as 12 machine learning (ML) algorithms, namely, logistic regression (LR), decision tree (DT), random forest (RF), gradient boosting (GB), AdaBoost, XGBoost, Naive Bayes (NB), support vector machine (SVM), light gradient-boosting machine (LightGBM), K-nearest neighbors (KNN), extremely randomized trees (ET), and voting classifier (VC), were performed. The model performance was evaluated using accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Shapley Additive exPlanations (SHAP) analysis was conducted to identify the most influential features contributing to mortality prediction.ResultsIn terms of AVGIB patients, extremely randomized trees model demonstrated excellent predictive value among other ML models, with the AUC of 0.996 ± 0.007, accuracy of 0.996 ± 0.009, precision of 0.957 ± 0.024, recall of 0.988 ± 0.012, and F1 score of 0.972 ± 0.007. The top 10 primary feature variables of ET model were whether combined with acute kidney failure, transfusion of albumin, vasoactive drugs, transfusion of plasma, transfusion of platelet, the max of international normalized ratio (INR), the max of prothrombin time (PT), and the max of activated partial thromboplastin time (APTT). In case of ANGIB patients, gradient boosting model proven to be the optimal machine learning models, with the AUC of 0.985 ± 0.002, accuracy of 0.948 ± 0.009, precision of 0.949 ± 0.009, recall of 0.968 ± 0.009, and F1 score of 0.959 ± 0.007. Similarly, the top 10 feature variables of GB model were Glasgow Coma Scale (GCS) score, vasoactive drugs, acute kidney failure, AIMS65 score, APACHE-II score, mechanical ventilation, the minimum of lactate, chronic liver disease, and the minimum and maximum of APTT. The SHAP visualization shows the weights of two ML models feature variables and the average sharp values of variables. Meanwhile, SHAP waterfall outputs the model prediction process with true positive and negative patients. Most importantly, two website prognostic prediction platforms were developed to enhance clinical accessibility: the ET model for AVGIB patients available at https://10zr656do5281.vicp.fun while the GB model for ANGIB patients accessible at http://10zr656do5281.vicp.fun.ConclusionThe ET model provides a reliable prognostic tool for AVGIB patients, while the GB model serves as a robust tool for ANGIB patients in predicting in-hospital mortality. By systematically integrating clinical features, risk stratification scores, vital signs, and invention measures, the ML models may deliver comprehensive predictions that benefit for clinical decision-making and potentially enhance clinical outcomes in the near future.

The prediction of wind energy generation is important to enhance the performance and dependability of renewable energy systems due to the rising demand for wind-generated electricity and advancements in wind energy technology competitiveness. This study leverages advanced machine learning (ML) and some other statistical and deep learning based time series forecasting models to enhance the accuracy of wind energy predictions. This comprehensive analysis includes nine ML models—Linear Regression, Random Forests (RF), Gradient Boosting Machines (GBM), Support Vector Machines (SVM), K-Nearest Neighbors (KNN), AdaBoost, XGBoost, Support Vector Regression (SVR), and Neural Networks—as well as Four time-series forecasting models—ARIMA, Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks and GRU. Each ML model underwent rigorous cross-validation to ensure optimal performance. The assessment criteria utilized here comprised the Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and the R² Score. It was found that among the nine ML models, Random Forests, GBM and KNN consistently provided superior accuracy and robustness, making them the top choices for wind energy prediction whereas the performance of linear regression, SVM and SVR were very poor for the considered dataset. From the experiment, Random Forest, GBM, and KNN showed the best performance with low MSE values of 0.77, 1.95, and 1.51 respectively, while other models had MSEs above 7.5, with AdaBoost reaching 30. Their RMSEs (0.88, 1.40, 1.23) and MAEs (0.093, 0.73, 0.10) also indicate strong predictive accuracy compared to the rest.In this paper, time series forecasting, TCNs, LSTM and GRU networks showed strong capabilities in capturing temporal dependencies and trends within the wind energy data. Visualization techniques were employed to compare model performances comprehensively, providing clear insights into their predictive power. Therefore, this present study offers a robust framework for researchers and practitioners aiming to leverage machine learning and time series forecasting in the realm of renewable energy prediction.

Background: Heart diseases continue to be among the primary causes of morbidity and mortality worldwide, highlighting the need for novel diagnostic methods to accurately, early, and economically predict and diagnose heart diseases. Current physical explores upon expert knowledge, possibly missing intricate and non-linear associations between heart disease risk factors. This is where machine learning (ML)methods emerge as promising alternatives for prediction and diagnosis of heart diseases by emphasizing data availability. Methods: This paper attempts to find out how six popular machine learning algorithms namely, Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting (GB), K-Nearest Neighbors (KNN), and eXtreme Gradient Boosting (XGB) perform on a publicly available heart disease dataset collected from Kaggle. For better performance and to make them comparable to each other, basic preprocessing techniques have also been implemented. The performance of all these algorithms will be measured by their respective definitions of accuracy, precision, recall, and F1 score. Result: With an F1-score of 98.52%, Random Forest and XGBoost surpassed other models, yielding a superior accuracy in predictions, measuring 98.53%. The models recorded a perfect level of precision, 100%, coupled with high values of recall, exceeding 97%. Gradient Boosting recorded impressive results, receiving an accuracy score of 93.17%. Conclusion: The research findings revealed that the Random Forest and XGBoost models are very effective in the prediction and diagnosis of heart disease. These two models have the capacity to support a clinical decision. Novelty: That is to say, the novelty of this research is that it evaluates multiple machine learning models with comparison to each other. The role of machine learning in enhancing cardiovascular disease diagnosis as well as decision support systems in healthcare and educating and aware the people about heart care and health.

In previous concrete applications, fly ash (FA), silica fume (SF), metakaolin (MK), ground granulated blast furnace slag (GGBS), waste glass powder (WGP), rice husk ash (RHA), ceramic waste powder (CWP), and marble powder (MP) have all been proven as potential industrial materials that can be utilized as supplementary cementitious material (SCM). Recently, the use of textile effluent sludge (TES) as SCM has drawn significant attention of several researchers for promoting sustainability. This study examines the prediction of compressive strength (CS) and tensile strength (TS) of concrete utilizing TES through several machine learning (ML) techniques, particularly random forest (RF), support vector machine (SVM), extreme gradient boost (XGBoost), and K‐nearest neighbors (KNN). Furthermore, deep learning techniques, involving convolutional neural networks and long short‐term memory networks, are utilized. Additionally, a hybrid machine learning (HML) model integrating RF and SVM algorithms as well as hybrid deep learning (HDL) models incorporating CNN and LSTM networks were developed for strength prediction. After hyperparameter tuning with the grid searching method, the standalone LSTM model has demonstrated superior performance in CS prediction (), achieving the highest accuracy compared to all ML, DL, and hybrid models whereas the standalone SVM model exhibited the highest accuracy among all models after tuning for TS prediction (). Notable, both hybrid frameworks exhibited competitive performance, demonstrating predictive accuracies comparable to the best standalone models, hybrid CNN‐LSTM model has shown the second‐highest accuracy () in terms of CS prediction the hybrid RF‐SVM model has been proven to possess greater predictive accuracy () than CNN‐LSTM in terms of TS prediction. In fact, this study has been designed to promote the high‐scale utilization of TES as SCM in concrete, which will be helpful for environmental pollution control.

Discrete choice models are widely used to explain transportation behaviors, including a household's decision to own a car. They show how some distinct choice of human behavior or preference influences a decision. They are also used to project future demand estimates to support policy exploration. This latter use for prediction is indirectly aligned with and conditional to the model's estimation which aims to fit the observed data. In contrast, machine learning models are derived to maximize prediction accuracy through mechanisms such as out-of-sample validation, non-linear structure, and automated covariate selection, albeit at the expense of interpretability and sound behavioral theory. We investigate how machine learning models can outperform discrete choice models for prediction of car ownership using transportation household survey data from Singapore. We compare our household car ownership model (multinomial logit model) against various machine learning models (e.g. Random Forest, Support Vector Machines) by using 2008 data to derive, i.e. estimate models that we then use to predict 2012 ownership. The machine learning models are inferior to the discrete choice model when using discrete choice features. However, after engineering features more appropriate for machine learning they are superior. These results highlight both the cost of applying machine learning models in econometric contexts and an opportunity for improved prediction and better urban policy making through machine learning models with appropriate features.

Background: Breast cancer is the most common malignancy among women worldwide, underscoring the importance of early detection and accurate prognostication. Machine learning (ML) has emerged as a promising approach, offering powerful tools for analyzing complex datasets in breast cancer prediction and diagnosis. Objective: This study evaluates the predictive performance of diverse ML algorithms for breast cancer classification using publicly available datasets, focusing on accuracy, interpretability, and generalizability. Methods: The dataset included clinical and demographic variables such as age, menopausal status, tumor size, and lymph node involvement. Data preprocessing addressed missing values and class imbalance, with the Synthetic Minority Oversampling Technique (SMOTE) applied to improve sensitivity for the minority class. Feature engineering involved interaction terms and scaling of numerical variables. Multiple ML models—Logistic Regression, Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine (SVM), Naive Bayes, K-Nearest Neighbors (KNN), and Neural Networks—were trained and evaluated. Performance was measured using sensitivity, F1-score, and AUC-ROC. Model interpretability was enhanced with SHapley Additive exPlanations (SHAP). Results: Random Forest achieved the best performance with an AUC-ROC of 0.9751, followed by Gradient Boosting (0.9242) and Neural Networks (0.9254). Logistic Regression and SVM yielded comparable results (0.9005 and 0.9344). Ensemble models showed higher accuracy and generalizability, particularly on external validation. Tumor size and lymph node involvement emerged as key predictors. SMOTE improved sensitivity across models. Conclusion: This study demonstrates the potential of ML in breast cancer prediction, emphasizing the effectiveness of ensemble methods and interpretability tools. Future work should focus on integrating ML into clinical practice for earlier detection and personalized treatment.

Background and purpose — External validation of machine learning (ML) prediction models is an essential step before clinical application. We assessed the proportion, performance, and transparent reporting of externally validated ML prediction models in orthopedic surgery, using the Transparent Reporting for Individual Prognosis or Diagnosis (TRIPOD) guidelines. Material and methods — We performed a systematic search using synonyms for every orthopedic specialty, ML, and external validation. The proportion was determined by using 59 ML prediction models with only internal validation in orthopedic surgical outcome published up until June 18, 2020, previously identified by our group. Model performance was evaluated using discrimination, calibration, and decision-curve analysis. The TRIPOD guidelines assessed transparent reporting. Results — We included 18 studies externally validating 10 different ML prediction models of the 59 available ML models after screening 4,682 studies. All external validations identified in this review retained good discrimination. Other key performance measures were provided in only 3 studies, rendering overall performance evaluation difficult. The overall median TRIPOD completeness was 61% (IQR 43–89), with 6 items being reported in less than 4/18 of the studies. Interpretation — Most current predictive ML models are not externally validated. The 18 available external validation studies were characterized by incomplete reporting of performance measures, limiting a transparent examination of model performance. Further prospective studies are needed to validate or refute the myriad of predictive ML models in orthopedics while adhering to existing guidelines. This ensures clinicians can take full advantage of validated and clinically implementable ML decision tools.

Recently, great progress has been achieved in diabetes management through minimally invasive continuous glucose monitoring, which involves piercing the skin to identify changes in glucose levels in the interstitial fluid. Nevertheless, the development of accurate fully noninvasive (NI) glucose monitors remains a topic of great interest. This work examines the processing of data collected from a multisensor system, which combines millimeter-wave (mm-wave), near-infrared (NIR), and other auxiliary sensors’ technology, coupled with a random forest machine learning (ML) model for prediction. We investigated possible correlations between mm-wave, NIR, skin temperature, pressure data, and blood glucose values and deployed an ML model for prediction. The model is based on the random forest algorithm that was trained and tested on data collected from a study involving five healthy human subjects undergoing an intravenous glucose tolerance test. Our model achieves a root mean square error of 21.06 mg/dl and a mean absolute relative difference of 7.31% for glucose prediction in the collected clinical subjects dataset. In addition, 96.1% of the model’s predictions fall within the clinically acceptable zones according to the Clarke error grid analysis. The preliminary results suggest that combining multiple sensor data with ML techniques is promising for the development of NI glucose monitoring.

BackgroundCardiovascular disease (CVD) is a leading cause of mortality among older adults, yet existing risk prediction tools lack validation for individuals aged ≥ 80 years. This study aims to develop and validate gender-specific machine learning models for CVD risk prediction in adults aged ≥ 80 years using routine biomarkers, and to identify key predictors through interpretable artificial intelligence approaches.MethodsThis prognostic study analyzed two independent longitudinal datasets (2012–2014 and 2014–2018) from the Chinese Longitudinal Healthy Longevity Survey including 1,954 community-dwelling adults aged ≥ 80 years (715 males, 1,239 females) without CVD at baseline. The outcome was incident CVD within 4 years. Five machine learning algorithms (i.e., logistic regression decision tree, support vector machine, random forest, and extreme gradient boosting (XGBoost)) were compared using area under the receiver operating characteristic curve (AUC), recall, specificity, precision, and F1-score. Model interpretability was assessed using SHapley Additive exPlanations values.ResultsThe XGBoost model demonstrated superior performance for both males (AUC [95% confidence interval], 0.751 [0.635–0.855]; recall, 759 [0.593–0.909]; F1-score, 0.506 [0.369–0.628]) and females (AUC, 0.748 [0.671–0.819]; recall, 0.891 [0.791–0.976]; F1-score, 0.463 [0.369–0.551]). Shared top predictors included vitamin D3, glycated albumin, platelet count, and vitamin B12. Risk stratification based on model predictions effectively identified high-risk individuals, with hazard ratios of 7.46 (95% CI, 2.81–19.78) for males and 5.18 (95% CI, 2.27–11.82) for females in the highest risk group compared to the lowest risk group.ConclusionsThis study developed interpretable, gender-specific machine learning models for CVD risk prediction in the oldest old population using routine biomarkers. The models demonstrated good discrimination and calibration, offering a practical tool for identifying high-risk individuals who may benefit from targeted preventive interventions. These findings suggest the potential utility of biomarker-based machine learning approaches in cardiovascular risk assessment for the rapidly growing elderly population.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12877-025-06676-0.

Forecasting Hematological Activity in Antiphospholipid Syndrome Using Predictive Models

Accurate precipitation forecasting is a vital task for many domains, such as agriculture, water management, flood prevention, and crop yield estimation. The use of machine learning (ML) approaches has improved precipitation forecasting accuracy, exhibiting promising results in capturing the intricate connections between various meteorological variables and precipitation patterns. However, given the vast array of available ML models, a comparative analysis is imperative for identifying the most effective models for precipitation prediction. This study aims to examine the capacities of ML algorithms to forecast precipitation based on weather data for the city of Casablanca, Morocco, which faces challenges in water management and climate change adaptation. Eight different ML models’ performances are compared: linear regression, polynomial regression, K-nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), random forest (RF), XGBoost, and an ensemble learning model. These models are evaluated based on their mean absolute error (MAE), mean squared error (MSE), and R-squared (R2 ) value to determine their effectiveness. The study showcases the potential of ML models in predicting precipitation by utilizing meteorological parameters such as temperature, humidity, wind speed, and pressure.

Development and validation of an explainable machine learning model for mortality prediction among patients with infected pancreatic necrosis.

647 Background: The primary treatment of most mNSTC is chemotherapy followed by surgery if the residual disease (RD) is >1 cm. However, conventional imaging lacks the specificity to characterize the tissue, often leading to overtreatment. This study hypothesizes that integrating CT-driven radiomics features with plasma miR371 and miR375 will enhance the predictive accuracy of Machine Learning (ML) models to predict teratoma, viable germ cell (vGCT) and fibrosis/necrosis (F/N) in mNSTC patients with RD. Methods: 111 lesions from52 patients, including residual teratoma (n=57), F/N (n=33), vGCT (n=10), and additional seminoma (n=11) for training purposes were included, split into training (N=78) and test cohorts (N=33). Lesions were lymph nodes (n=87), lung (n=21), and brain (n=3) with a median size of 1.6 cm (Q1-Q3 interval=1.2-2.73 cm). 3D Slicer version 5.6.1 was used to segment the RD > 1 cm (short axis) and extract radiomics features. Plasma miRNA levels before resection were measured by RT-PCR. Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and CatBoost (CB) ML models were evaluated to define the operating characteristics of radiomics alone (R-only) and in combination with miR371 (371) and/or miR375 (375) levels in predicting teratoma, vGCT and F/N. Results: For predicting teratoma, the best models were RF (R+375 and R+371+375), CB (R+371+375), and GB (R+371 and R+371+375). While adding miR371 or miR375 to R-only slightly improved AUC across models, the best results were achieved with the R+375+371 dataset. CB achieved AUCs ranging from 0.94 to 0.97 in training and 0.81 to 0.93 in test sets, with its highest AUC of 0.93 (95% CI: 0.78-0.97) on the R+375+371 dataset to differentiate all three classes. Similarly, GB demonstrated strong performance, achieving its highest AUC of 0.93 (95% CI: 0.79-0.96) on the R+375+371 dataset (Table). Conclusions: Integration of plasma miR371, miR375 and radiomics improved accuracy of predicting histologies across all ML models. These methods could be used to characterize the histology of RD in mNSTC patients to better inform treatment decisions. Further refinement, including incorporation of histological findings of the primary tumor, will be reported. AUC values of different ML algorithms on training and test sets. TRAINING SET TEST SET Model ±SD R R+375 R+371 R+375+371 Model (95% CI) R R+375 R+371 R+375+371 RF 0.93±0.05 0.95±0.04 0.95±0.03 0.96±0.04 RF 0.8(0.59-0.89) 0.85(0.72-0.93) 0.87(0.76-0.95) 0.91(0.78-0.95) SVM 0.84±0.06 0.84±0.09 0.89±0.11 0.89±0.09 SVM 0.72(0.54-0.80) 0.74(0.56-0.82) 0.83(0.69-0.92) 0.84(0.76-0.94) GB 0.94±0.04 0.91±0.08 0.95±0.05 0.97±0.03 GB 0.84(0.61-0.96) 0.89(0.77-0.97) 0.89(0.79-0.96) 0.93(0.79-0.96) CB 0.95±0.03 0.94±0.03 0.94±0.04 0.97±0.03 CB 0.81(0.6-0.93) 0.86(0.73-0.94) 0.89(0.78-0.97) 0.93(0.78-0.97)