Jul 2026· Journal International Review of Research Studies· 0 citations
TL;DR
It is demonstrated that machine learning models can effectively identify high-risk patients and that minority data resampling significantly improves mortality classification reliability, and the approach offers potential value for clinical decision support systems and prioritised care pathways.
Abstract
Heart failure continues to account for high global mortality rates despite improvements in cardiovascular medicine. Early mortality risk detection is essential for healthcare resource planning and personalised intervention. This study develops a machine learning framework for mortality prediction in patients with chronic heart failure, using demographic, clinical and biochemical data. A dataset containing 299 patient records and 13 biomarkers was pre-processed, normalised, and evaluated to identify feature-outcome relationships. Several modelling strategies were assessed, including Random Forest and Logistic Regression. Class imbalance was handled using Tomek Links, SMOTE and Random Oversampling to reduce decision boundary distortion. Random Forest trained on Tomek Links rebalanced data produced the strongest performance, achieving test accuracy of 0.91 and a ROC-AUC of 0.94. Logistic Regression achieved lower accuracy (0.82) and weaker minority recall. The results highlight the effect of biomarker depth on model separability, where serum creatinine, ejection fraction and age were the strongest predictors. The outcomes demonstrate that machine learning models can effectively identify high-risk patients and that minority data resampling significantly improves mortality classification reliability. The approach offers potential value for clinical decision support systems and prioritised care pathways.
Male sex was a statistically significant independent predictor of heart disease after controlling for other clinical variables and the findings support sex-specific screening and preventive strategies for high-cholesterol male patients and demonstrate the value of interpretable machine learning models for clinical decision support.
Taiwo Samson Adeyemo· GSC Advanced Research and Re...· 0 citations
Heart failure (HF) remains one of the leading causes of mortality worldwide, making early prediction and diagnosis essential for improving patient survival and reducing healthcare costs. Machine learning (ML) techniques have demonstrated considerable potential in assisting clinicians with accurate disease prediction. However, most heart failure datasets suffer from class imbalance, which negatively affects classification performance, particularly for minority class patients. This paper presents an optimized Extreme Gradient Boosting (XGBoost) model integrated with the Synthetic Minority Over-sampling Technique (SMOTE) for heart failure patient classification. Initially, missing values, outliers, and redundant attributes are removed through preprocessing. SMOTE is then applied to balance the dataset by generating synthetic minority samples. Hyperparameter optimization using Grid Search with Stratified Cross-Validation identifies the optimal XGBoost parameters. The proposed framework is evaluated using Accuracy, Precision, Recall, F1-score, ROC-AUC, and Matthews Correlation Coefficient (MCC). Experimental results demonstrate that the optimized XGBoost-SMOTE model significantly outperforms traditional machine learning algorithms including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, KNearest Neighbors, AdaBoost, and baseline XGBoost. The proposed approach achieves an accuracy of 98.21%, precision of 97.94%, recall of 98.47%, F1-score of 98.20%, and ROC-AUC of 99.10%, indicating superior predictive capability for heart failure diagnosis. These findings suggest that integrating SMOTE with optimized XGBoost provides an effective decision-support tool for clinical risk assessment. Similar findings have been reported in prior studies evaluating XGBoost with SMOTE-based preprocessing for heart failure prediction.
B. Naveen, N. Rao· International Journal of Eng...· 0 citations
Pro data from multiple follow-ups, combined with a model constructed using GRU, provides promising tool for predicting mortality risk in patients with chronic heart failure, and the self-developed web-based decision support system allows users to calculate risk scores simply by entering patient information.
Yujia Zhang, Mengyi Dou, Fengqin Ding et al.· Vascular Health and Risk Man...· 0 citations
OBJECTIVE
To develop and validate a machine learning model for predicting ICU mortality in CHF patients with pulmonary infection.
METHODS
Clinical data were extracted from the MIMIC-IV database, and ICU mortality within 15 days was defined as the primary endpoint. Patients were stratified into HFrEF, HFmrEF, and HFpEF based on left ventricular ejection fraction. Six models were constructed-logistic regression, random forest, light gradient boosting machine, extreme gradient boosting (XGBoost), multilayer perceptron, and k-nearest neighbor-and optimized using cross-validated grid search. Predictive performance was evaluated using accuracy, precision, recall, F1-score, AUC, Brier score, Youden index, and calibration slope. Model interpretability was assessed using SHAP and LIME. External validation was conducted using the eICU-CRD database.
RESULTS
A total of 1308 patients were included, with an ICU mortality of 15.98%. Among all models, XGBoost achieved the best balance of discrimination and calibration (AUC 0.968; calibration slope 1.070), and retained useful but attenuated discrimination in external testing (AUC 0.718). SHAP analysis identified lactic acid, white blood cell count, prior diagnosis of hypertension, serum potassium, and pre-existing diabetes mellitus as the five most influential predictors of the outcome. Subgroup analyses further demonstrated distinct pathophysiological trajectories associated with mortality: HFrEF predominantly exhibited hemodynamic instability, impaired tissue perfusion, and electrolyte disturbances; in contrast, HFpEF were more frequently associated with chronic metabolic-inflammatory dysregulation and coagulopathy.
CONCLUSION
The XGBoost-based model provides interpretable prediction of ICU mortality in CHF patients with pulmonary infection. Phenotype-specific interpretability analysis further suggested that mortality risk mechanisms differ between heart failure subtypes, supporting more individualized risk assessment and management in critically ill CHF patients.
Yihai Zhai, Bingbing Wei, Dan Lan et al.· BMC Medical Informatics and...· 0 citations
Hypertension, commonly known as high blood pressure, is a major risk factor for cardiovascular diseases and premature mortality worldwide. Early detection and prevention are critical in reducing its health impact. This study explores the application of machine learning (ML) techniques to predict the likelihood of hypertension in individuals using clinical and demographic data. A variety of supervised learning algorithms, including Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting, were evaluated for their predictive performance [1]. The dataset was preprocessed through feature selection, normalization, and handling of missing values to improve model accuracy.[2] Performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC were used to assess the models [4]. The results demonstrate that ML models can effectively identify individuals at high risk of hypertension, offering a valuable tool for early intervention and personalized healthcare [5]. This approach underscores the potential of artificial intelligence in supporting public health efforts and enhancing clinical decision-making.
Key words: Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting.
G. Vamsi, K. Bhargavi· International Scientific Jou...· 0 citations
Chronic diseases remain a major cause of mortality and long-term disability, and proactive identification of high-risk individuals is difficult because early clinical changes are often subtle, incomplete, and distributed across heterogeneous hospital records. This study proposes a machine-learning-based predictive analytics framework for early detection of chronic disease risk using electronic health records, laboratory profiles, demographic factors, medication history, and derived clinical indicators. The study used 48,320 adult patient records collected from four tertiary hospitals between 2014 and 2023, with leakage-controlled preprocessing, multistage missing-data handling, correlation and SHAP-assisted feature selection, and stratified model development. Logistic Regression, Random Forest, XGBoost, Multilayer Perceptron, and TabNet were evaluated against clinical risk-score baselines using AUROC, PR-AUC, F1-score, recall, calibration, Brier score, and stability under missingness and imbalance. XGBoost achieved the strongest internal performance with AUROC of 0.942, PR-AUC of 0.901, F1-score of 0.901, and Brier score of 0.108. External validation on 12,950 patients from an unseen hospital produced AUROC of 0.931, confirming limited performance degradation and improved generalizability. SHAP analysis identified creatinine, HbA1c, age, systolic blood pressure, and triglycerides as dominant contributors, supporting clinically interpretable early-risk alerts for preventive care.
P. A. Prakash, Mamtha C, Manishathri R et al.· 2026 7th International Confe...· 0 citations