The reviewed studies demonstrated that Deep learning approaches, particularly convolutional neural networks, transformer-based models, and multimodal frameworks, showed improved predictive accuracy when large and diverse datasets were available.
Abstract
Machine learning (ML) and deep learning (DL) models are increasingly applied for disease prediction, diagnosis, and risk stratification across a wide range of medical conditions. This review explores current breakthroughs in ML and DL-based disease prediction, with a particular focus on cancer, diabetes, and cardiovascular diseases (CVDs), Alzheimer’s, Parkinson’s disease (PD) and chronic kidney diseases (CKD). A narrative review with structured literature selection was conducted using PubMed and Google Scholar, focusing on studies from 2021 to 2026, articles were included based on their relevance with machine and deep learning in disease prediction. The reviewed studies demonstrated that Deep learning approaches, particularly convolutional neural networks, transformer-based models, and multimodal frameworks, showed improved predictive accuracy when large and diverse datasets were available. Explainable AI techniques enhanced model transparency, while federated learning emerged as promising solution for privacy-preserving model development. However, challenges related to data heterogeneity, limited external validation, class imbalance, model interpretability, and clinical integration remain significant barriers to widespread implementation. Overall, this review highlights the significant potential of ML-DL driven disease prediction while emphasizing the need for robust validation, transparency, clinical integration to ensure safe and effective-real world applications.
A framework that integrates multiple machine learning models and optimization algorithms to enable the early prediction of cardiovascular disease, Parkinson’s disease (PD), and nonalcoholic fatty liver disease (NAFLD) and has high potential for adoption in health-care applications driven by artificial intelligence.
Cardiovascular disease is a main cause of mortality worldwide that need the development of reliable and data-driven prediction models for timely diagnosis and intervention. Conventional risk assessment methods depend on statistical scoring and handcrafted clinical features fail to capture complex nonlinear features in several cardiovascular existing data. This systematic literature review investigates recent advances in artificial intelligence-based CVD prediction that include machine learning, deep learning, hybrid, and ensemble learning models. The PRISMA-2020 strategy and Kitchenham guidelines, 110 studies published between 2013 and 2025 were systematically analyzed across diverse data modalities such as electronic health records, electrocardiograms, medical images, wearable sensors, and multimodal datasets. The findings indicate that tree-based ML models such as Random Forest, XGBoost, and Gradient Boosting shows the strong performance on structured clinical datasets. The DL-based models such as convolutional neural networks, long short-term memory networks, and transformer-based models effectively capture the high-dimensional physiological and imaging features. Hybrid model combined deep representations with clinical parameters to achieve predictive performance up to 98.00%, and ensemble learning models shows the superior performance and generalization capabilities across several distinct datasets. The challenges in cardiovascular disease detection such as limited multimodal datasets, very few external validation, insufficient explainability, and the absence of standardized benchmark evaluation parameters continue to show down the clinical translation. This review synthesizes current algorithmic developments, identifies critical research gaps, and highlights the potential of multimodal learning, explainable AI, federated learning, and clinically validated model for advancing next-generation cardiovascular risk prediction systems.
A. Gaikwad, N. Kulkarnir· Journal of Intelligent Decis...· 0 citations
INTRODUCTION
The main concern of the world's public health is cardiovascular disease. There are many different types of machine learning models available to help clinicians identify cardiovascular disease at an early stage and predict future risk. In this study, we propose HCVDNet, a hybrid machine learning-deep learning model that can be used to improve the predictive accuracy of models as well as provide clinicians with clear explanations of the results of their predictions.
METHODS
The proposed architecture is a combination of three ensemble learning algorithms (XGBoost, CatBoost, and Random Forest) to learn features from data in the first step; then, the second step uses Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to create a deep context model of the data. We have integrated two post-hoc explainable artificial intelligence techniques (SHAP and LIME) into our architecture to produce explanations at both the global and instance levels of data. Our experiments will use cardiovascular- related datasets found in the UCI and Kaggle databases.
RESULTS
The performance of the proposed framework was significantly superior to that of other baseline machine learning and deep learning-based architectures in terms of accuracy, as it reached 96.94%, AUC reached 98.72%, and ECE was 0.021. As such, SHAP and LIME were used to evaluate the model's output to determine whether there were any clinically relevant and, therefore, reliable feature patterns within its outputs.
DISCUSSION
The findings indicate that incorporating ensemble learning with deep temporal modeling enhances both the accuracy of predictive capabilities and the interpretability of these predictions. The interpretability modules further support physician confidence in the AI-assisted decision-making process by providing a clear explanation of how each feature contributes to the medically meaningful outputs generated from the model.
CONCLUSION
As a result, H-CVDNet is capable of developing a high-performance, highly interpretable diagnostic framework for predicting cardiovascular risk. In addition, because it is designed to be FHIR compliant, it can seamlessly integrate into existing electronic health record systems. Thus, there appears to be considerable opportunity to deploy this type of AI-based framework in real-time clinical settings and provide additional, dependable AI-based healthcare analytics.
Bhaskar Adepu, T. Archana· Cardiovascular & Haematologi...· 0 citations
Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for approximately 17.9 million deaths annually according to the World Health Organization. Early and accurate risk assessment is critical for timely intervention and improved patient outcomes. However, existing machine learning and deep learning approaches face significant limitations including poor modeling of nonlinear relationships, inadequate feature interaction learning, lack of uncertainty quantification, class imbalance challenges, overfitting tendencies, limited interpretability, insufficient multimodal fusion capabilities, poor cross-dataset generalization, absence of attention-based learning mechanisms, and inadequate clinical explainability. To address these critical gaps, we propose CardioAttentionNet, a novel hybrid deep learning framework that integrates residual convolutional neural networks, transformer encoders with multi-head self-attention, bidirectional long short-term memory networks, and cross-attention modules for comprehensive cardiovascular risk prediction. The framework incorporates intelligent data preprocessing, hybrid feature engineering with attention-based feature selection, adaptive feature fusion with learnable weights, channel attention mechanisms, probability calibration, and explainable AI components including SHAP, LIME, and Integrated Gradients. Evaluated on a large-scale multi-source dataset comprising 75,000 samples from UCI Cleveland, Framingham Heart Study, Kaggle, and Cardiovascular Disease datasets, CardioAttentionNet achieved exceptional performance with 98.12% accuracy, 97.84% precision, 98.37% recall, 97.93% specificity, 98.10% F1-score, 0.983 AUC, 0.962 MCC, and 0.961 Cohen’s Kappa, significantly outperforming 13 baseline methods. Comprehensive ablation studies, statistical validation, and explainability analysis demonstrate the framework’s clinical utility for early heart disease risk stratification and decision support in real-world healthcare settings.
Swapnil Hiralal Chaudhari, Anish Kumar Choudhary· International journal of com...· 0 citations
The paper presents the framework that mediates between optimization methods and predictive modeling to provide valuable information on the next generation of data-driven cardiovascular diagnostics to provide valuable information on the next generation of data-driven cardiovascular diagnostics.
Shamal Salunkhe, Smita Bharne, P. Patil et al.· International Research Journ...· 0 citations
Cardiovascular Disease (CVD) remains one of the leading causes of mortality worldwide, emphasizing the need for accurate and early diagnostic solutions. Recent advances in Machine Learning (ML) and Deep Learning (DL) have shown significant potential to support clinical decision-making through data-driven prediction models. This study presents a robust Ensemble Learning (EL) framework for the prediction and classification of CVD by integrating multiple ML algorithms with a DL component. Specifically, an Artificial Neural Network (ANN) is employed as a feature extraction layer prior to ensemble aggregation using techniques such as Random Forest, XGBoost, and LightGBM. The proposed approach is evaluated using accuracy, precision, recall, F1-score, and AUC-ROC. Experimental results on a benchmark dataset demonstrate that the model achieves a high accuracy of 98.8%, outperforming individual classifiers and existing approaches. The integration of ANN-based feature extraction enhances model generalization and reduces prediction error. These findings highlight the effectiveness of the proposed framework for early heart disease detection and clinical decision support.
El Haddad Khadija, A. Bekkari, W. Bouarifi et al.· Engineering, Technology &...· 0 citations