Heart failure forecasting has garnered significant attention recently due to its potential to enhance patient care and improve healthcare resource allocation. Numerous studies have explored the application of machine learning and deep learning techniques in predicting the onset and progression of heart failure. These methods leverage the abundance of clinical and physiological data available, aiming to provide early and accurate prognostic insights for clinicians and patients. In this section, we review the literature on heart failure forecasting, focusing on the various machine learning and deep learning approaches employed, the datasets utilized, and the reported performance metrics.

Liang et al. [16] proposed a novel deep learning model called tBNA-PR to accurately predict heart failure and identify sub-phenotypes using temporal electronic health records (tEHRs) data. The model effectively captures the complexity and heterogeneity of the data to obtain informative patient representations. The study demonstrates the effectiveness of tBNA-PR on a real-world dataset, achieving prediction accuracy of 0.78, F1-Score of 0.7671, and AUC of 0.7198, outperforming existing benchmarks. The analysis identifies three distinct sub-phenotypes of heart failure patients based on clustering and subgroup analysis, revealing specific characteristics and significant features associated with each sub-phenotype. The findings have practical implications for clinical decision support, but the study acknowledges limitations related to data completeness, disease specificity, generalizability, interpretability, and the need for further research.

In a study by Robert et al. [7], a novel algorithm was proposed for diagnosing coronary artery disease, employing a probability-based approach. This algorithm’s reliability and clinical utility were tested across three patient test groups. 303 consecutive patients who were sent for coronary angiography at the Cleveland Clinic between May 1981 and September 1984 served as the reference group for the model’s development. The study’s findings showed that when applied to individuals with chest pain syndromes and intermediate disease prevalence, discriminant functions used to determine coronary disease probabilities produced accurate and clinically helpful results. In another study by Simge et al. [8], a comparison was made between two prominent machine learning platforms using the same dataset. The researchers conducted experiments to classify heart disease using six distinct algorithms: Quadratic SVM, Linear SVM, Cubic SVM, Decision Tree, Medium Gaussian SVM, and Ensemble Subspace Discriminant. These experiments were carried out in both the Matlab environment and WEKA. The dataset utilized in this study was acquired from the machine learning repository of UCI [12]. The highest accuracy achieved was 67.7% using the Ensemble Subspace Discriminant algorithm in Matlab, while the Decision Tree algorithm in the WEKA platform also yielded an accuracy of 67.7%.

Li et al. [17] introduce a deep learning-based automatic system for diagnosing heart failure by tackling the issue of imbalanced data in chest X-ray (CXR) images. The approach combines under-sampling and instance selection techniques to maintain the integrity of data distribution and presents a comprehensive multi-level classification method to diagnose specific heart failure causes. Experimental results demonstrate that the proposed approach outperforms traditional under-sampling methods, achieving an accuracy of 84.44% in multi-class classification tasks. Rao et al. [18] presented a deep-learning framework for predicting heart failure incidence using electronic health records. The authors developed a novel Transformer-based risk model incorporating patient diagnoses, medications, age, and calendar year. The model achieved high predictive performance, outperforming existing deep learning models. Ablation analysis revealed the importance of medications and calendar year in predicting HF risk. Contribution analyses identified both established risk factors and new associations, providing insights for data-driven risk factor identification. The study highlights the potential of the deep learning model to inform preventive care and identify new hypotheses for further research and drug repurposing studies in HF prediction and other complex conditions.

In a related study conducted by Ali et al. [9], the Cleveland dataset was employed for analysis, and a feature selection process was carried out to train three distinct classifiers, namely Support Vector Machine (SVM), Naïve Bayes, and K-Nearest Neighbors, utilizing a 10-fold cross-validation technique. Their findings revealed that the Naïve Bayes classifier exhibited superior performance on this dataset and the selected features, surpassing or equaling the performance of SVM and KNN across all four evaluation parameters. Notably, it achieved an accuracy rate of 84%. Saba et al. conducted a study [11] that explores the prediction of heart disease using data science methodologies. Their research focuses on employing feature selection techniques and algorithms to improve the accuracy of heart disease prediction. Multiple heart disease datasets were utilized for experimentation and analysis purposes. The authors employed various feature selection techniques, including Logistic Regression, Decision Tree, Random Forest, Nave Bayes, and Logistic Regression SVM, using Rapid Miner as the tool. Notably, the highest achieved accuracy of 84.85% was obtained using the UCI dataset [12] in combination with the Logistic Regression (SVM) technique.