The traditional complications of diabetes are well known and continue to pose a considerable burden to millions of people with diabetes mellitus (DM). With the continuous accumulation of medical data and technological advances, artificial intelligence has shown great potential and advantages in the prediction, diagnosis, and treatment of DM. When DM is diagnosed, some subjective factors and diagnostic methods of doctors will have an impact on the diagnostic results, so the use of artificial intelligence for fast and effective early prediction of DM patients can provide decision-making support to doctors and give more accurate treatment services to patients in time, which is of great clinical medical significance and practical significance. In this paper, an adaptive Stacking ensemble model is proposed based on the theory of \"error-ambiguity decomposition,\" which can adaptively select the base classifiers from the pre-selected models. The adaptive Stacking ensemble model proposed in this paper is compared with KNN, SVM, RF, LR, DT, GBDT, XGBoost, LightGBM, CatBoost, MLP and traditional Stacking ensemble models. The results showed that the adaptive Stacking ensemble model achieved the best performance in five evaluation metrics: accuracy, precision, recall, F1 value and AUC value, which were 0.7559, 0.7286, 0.8132, 0.7686 and 0.8436. The model can effectively predict DM patients and provide a reference value for the screening and diagnosis of clinical DM.

BACKGROUND: There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure (HF).
OBJECTIVE: To create a stacking model for predicting depression in patients with HF.
METHODS: This study analyzed data on 1084 HF patients from the National Health and Nutrition Examination Survey database spanning from 2005 to 2018. Through univariate analysis and the use of an artificial neural network algorithm, predictors significantly linked to depression were identified. These predictors were utilized to create a stacking model employing tree-based learners. The performances of both the individual models and the stacking model were assessed by using the test dataset. Furthermore, the SHapley additive exPlanations (SHAP) model was applied to interpret the stacking model.
RESULTS: The models included five predictors. Among these models, the stacking model demonstrated the highest performance, achieving an area under the curve of 0.77 (95%CI: 0.71-0.84), a sensitivity of 0.71, and a specificity of 0.68. The calibration curve supported the reliability of the models, and decision curve analysis confirmed their clinical value. The SHAP plot demonstrated that age had the most significant impact on the stacking model\'s output.
CONCLUSIONS: The stacking model demonstrated strong predictive performance. Clinicians can utilize this model to identify high-risk depression patients with HF, thus enabling early provision of psychological interventions.

OBJECTIVE: We identified predictive factors and developed a novel machine learning (ML) model for predicting mortality risk in patients with sepsis-associated encephalopathy (SAE).
METHODS: In this retrospective cohort study, data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) and eICU Collaborative Research Database were used for model development and external validation. The primary outcome was the in-hospital mortality rate among patients with SAE; the observed in-hospital mortality rate was 14.74% (MIMIC IV: 1112, eICU: 594). Using the least absolute shrinkage and selection operator (LASSO), we built nine ML models and a stacking ensemble model and determined the optimal model based on the area under the receiver operating characteristic curve (AUC). We used the Shapley additive explanations (SHAP) algorithm to determine the optimal model.
RESULTS: The study included 9943 patients. LASSO identified 15 variables. The stacking ensemble model achieved the highest AUC on the test set (0.807) and 0.671 on external validation. SHAP analysis highlighted Glasgow Coma Scale (GCS) and age as key variables. The model (https://sic1.shinyapps.io/SSAAEE/) can predict in-hospital mortality risk for patients with SAE.
CONCLUSIONS: We developed a stacked ensemble model with enhanced generalization capabilities using novel data to predict mortality risk in patients with SAE.

With the ongoing process of industrialization, the issue of declining air quality is increasingly becoming a critical concern. Accurate prediction of the Air Quality Index (AQI), considered as an all-inclusive measure representing the extent of pollutants present in the atmosphere, is of paramount importance. This study introduces a novel methodology that combines stacking ensemble and error correction to improve AQI prediction. Additionally, the reptile search algorithm (RSA) is employed for optimizing model parameters. In this study, four distinct regional AQI data containing a collection of 34864 data samples are collected. Initially, we perform cross-validation on ten commonly used single models to obtain prediction results. Then, based on evaluation indices, five models are selected for ensemble. The results of the study show that the model proposed in this paper achieves an improvement of around 10% in terms of accuracy when compared to the conventional model. Thus, the model introduced in this study offers a more scientifically grounded approach in tackling air pollution.

UNASSIGNED: The Eastern Cooperative Oncology Group performance status (ECOG PS) is a widely recognized measure used to assess the functional abilities of cancer patients and predict their prognosis. It plays a crucial role in guiding treatment decisions made by physicians. This study aimed to build a stacking ensemble-based prognosis predictor model for predicting the ECOG PS of a liver cancer patient undergoing treatment.
UNASSIGNED: We used Light Gradient Boosting Machine (LightGBM) as the meta-model, and five base models, including Random Forest (RF), Extra Trees (ET), AdaBoost (Ada), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost). After preprocessing the data and applying feature selection method, the stacking ensemble model was trained using 1622 liver cancer patients\' data and 46 variables. We also integrated the stacking ensemble model with a LIME-based explainable model to obtain model prediction explainability.
UNASSIGNED: According to the research, the best combination of the stacking ensemble model is ET + XGBoost + RF + GBM + Ada + LightGBM and achieved a ROC AUC of 0.9826 on the training set and 0.9675 on the test set.
UNASSIGNED: This explainable stacking ensemble model can become a helpful tool for objectively predicting ECOG PS in liver cancer patients and aiding healthcare practitioners to adapt their treatment approach more effectively.

This research employs machine learning analysis on extensive data from a prominent Korean life insurance company to substantiate the insurance demand theory, which posits that insurance demand increases with risk aversion. We quantitatively delineate the traits of risk-averse individuals. Our study focuses on a cohort of 94,306 individuals who have filed insurance claims due to illness. To forecast prospective insurance consumers inclined toward additional purchases, we construct a predictive model using a machine learning algorithm. This model incorporates 19 demographic and socioeconomic factors as independent variables, with additional insurance acquisition as the dependent variable. Consequently, we uncover the distinctive characteristics of consumers predicted to acquire supplementary insurance products. Our findings reveal a significant association between the independent variables and the likelihood of purchasing additional insurance. Notably, 10 out of the 19 independent variables exert a substantial influence on additional insurance acquisitions. These characteristics encompass residence in rural areas, a higher likelihood of being female, advanced age, increased assets, a higher likelihood of being blue-collar workers, lower education levels, a greater likelihood of being married or divorced/separated, a history of cancer, and a predisposition for existing policyholders with prior subscriptions to actual loss insurance or substantial insurance contract amounts. Our study holds academic significance by addressing limitations observed in prior research, which predominantly relied on questionnaires to qualitatively assess risk aversion. Instead, we offer specific insights into individual characteristics associated with risk aversion. Moreover, we anticipate that Korean insurance companies can leverage these insights to attract new clientele while retaining existing members through predictive risk aversion analysis. These findings also offer valuable insights across a spectrum of disciplines, including business administration, psychology, education, sociology, and sales/marketing, related to individuals\' risk preferences and behaviors.

Indoleamine 2,3-dioxygenase 1 (IDO1) is viewed as an extremely promising target for cancer immunotherapy. Here, we proposed a two-layer stacking ensemble model, IDO1Stack, that can efficiently predict IDO1 inhibitors. First, we constructed a series of classification models based on five machine learning algorithms and eight molecular characterization methods. Then, a stacking ensemble model was built using the top five models as the base classifier and logistic regression as the meta-classifier. The areas under the receiver operating characteristic curve (AUC) of IDO1Stack on the test set and external validation set were 0.952 and 0.918, respectively. Furthermore, we computed the applicability domain and privileged substructures of the model and interpreted the model using SHapley Additive exPlanations (SHAP). It is expected that IDO1Stack can well study the interaction between target and ligand, providing practitioners with a reliable tool for rapid screening and discovery of IDO1 inhibitors.

With the prevalence of cerebrovascular disease (CD) and the increasing strain on healthcare resources, forecasting the healthcare demands of cerebrovascular patients has significant implications for optimizing medical resources.
In this study, a stacking ensemble model comprised of four base learners (ridge regression, random forest, gradient boosting decision tree, and artificial neural network) and a meta learner (elastic net) was proposed for predicting the daily number of hospital admissions (HAs) for CD using the historical HAs data, air quality data, and meteorological data in Chengdu, China from 2015 to 2018. To solve the label imbalance problem, a re-weighting method based on label distribution smoothing was integrated into the meta learner. We trained the model using the data from 2015 to 2017 and evaluated its predictive ability using the data in 2018 based on four metrics, including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination (R2). In addition, the SHapley Additive exPlanations (SHAP) framework was applied to provide explanation for the prediction of our stacking model.
Our proposed model outperformed all the base learners and long short-term memory (LSTM) on two datasets. Particularly, compared with the optimal results obtained by individual models, the MAE, RMSE, and MAPE of the stacking model decreased by 13.9%, 12.7%, and 5.8%, respectively, and the R2 improved by 6.8% on CD dataset. The model explanation demonstrated that environmental features played a role in further improving the model performance and identified that high temperature and high concentrations of gaseous air pollutants might strongly associate with an increased risk of CD.
Our stacking model considering environmental exposure is efficient in predicting daily HAs for CD and has practical value in early warning and healthcare resource allocation.

Numerous studies have identified risk factors for physical restraint (PR) use in older adults in long-term care facilities. Nevertheless, there is a lack of predictive tools to identify high-risk individuals.
We aimed to develop machine learning (ML)-based models to predict the risk of PR in older adults.
This study conducted a cross-sectional secondary data analysis based on 1026 older adults from 6 long-term care facilities in Chongqing, China, from July 2019 to November 2019. The primary outcome was the use of PR (yes or no), identified by 2 collectors\' direct observation. A total of 15 candidate predictors (older adults\' demographic and clinical factors) that could be commonly and easily collected from clinical practice were used to build 9 independent ML models: Gaussian Naïve Bayesian (GNB), k-nearest neighbor (KNN), decision tree (DT), logistic regression (LR), support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), extreme gradient boosting (XGBoost), and light gradient boosting machine (Lightgbm), as well as stacking ensemble ML. Performance was evaluated using accuracy, precision, recall, an F score, a comprehensive evaluation indicator (CEI) weighed by the above indicators, and the area under the receiver operating characteristic curve (AUC). A net benefit approach using the decision curve analysis (DCA) was performed to evaluate the clinical utility of the best model. Models were tested via 10-fold cross-validation. Feature importance was interpreted using Shapley Additive Explanations (SHAP).
A total of 1026 older adults (mean 83.5, SD 7.6 years; n=586, 57.1% male older adults) and 265 restrained older adults were included in the study. All ML models performed well, with an AUC above 0.905 and an F score above 0.900. The 2 best independent models are RF (AUC 0.938, 95% CI 0.914-0.947) and SVM (AUC 0.949, 95% CI 0.911-0.953). The DCA demonstrated that the RF model displayed better clinical utility than other models. The stacking model combined with SVM, RF, and MLP performed best with AUC (0.950) and CEI (0.943) values, as well as the DCA curve indicated the best clinical utility. The SHAP plots demonstrated that the significant contributors to model performance were related to cognitive impairment, care dependency, mobility decline, physical agitation, and an indwelling tube.
The RF and stacking models had high performance and clinical utility. ML prediction models for predicting the probability of PR in older adults could offer clinical screening and decision support, which could help medical staff in the early identification and PR management of older adults.

BACKGROUND: Many long non-coding RNAs (lncRNAs) have key roles in different human biologic processes and are closely linked to numerous human diseases, according to cumulative evidence. Predicting potential lncRNA-disease associations can help to detect disease biomarkers and perform disease analysis and prevention. Establishing effective computational methods for lncRNA-disease association prediction is critical.
RESULTS: In this paper, we propose a novel model named MAGCNSE to predict underlying lncRNA-disease associations. We first obtain multiple feature matrices from the multi-view similarity graphs of lncRNAs and diseases utilizing graph convolutional network. Then, the weights are adaptively assigned to different feature matrices of lncRNAs and diseases using the attention mechanism. Next, the final representations of lncRNAs and diseases is acquired by further extracting features from the multi-channel feature matrices of lncRNAs and diseases using convolutional neural network. Finally, we employ a stacking ensemble classifier, consisting of multiple traditional machine learning classifiers, to make the final prediction. The results of ablation studies in both representation learning methods and classification methods demonstrate the validity of each module. Furthermore, we compare the overall performance of MAGCNSE with that of six other state-of-the-art models, the results show that it outperforms the other methods. Moreover, we verify the effectiveness of using multi-view data of lncRNAs and diseases. Case studies further reveal the outstanding ability of MAGCNSE in the identification of potential lncRNA-disease associations.
CONCLUSIONS: The experimental results indicate that MAGCNSE is a useful approach for predicting potential lncRNA-disease associations.