Aug 2026· STEM Fellowship Journal· pp. 1-9· 0 citations
TL;DR
A novel multi-model approach was used in which ten additional neural networks were trained separately and used to predict the conditions through bootstrapping and bagging techniques, and could potentially aid in enabling early disease prediction, facilitating timely treatment, and improving quality of life.
Abstract
Prognostic modelling is an essential tool in clinical sciences. A machine learning approach can efficiently help with disease prediction and prevention by integrating large amounts of structured patient records. This study develops a deep learning framework to assist in early disease detection and clinical decision-making. A hospital patient dataset was used to develop a predictive model for 13 chronic conditions. The dataset was preprocessed, including the use of a denoising autoencoder neural network for imputations. To improve the predictive ability of the model and reduce overfitting, a novel multi-model approach was used in which ten additional neural networks were trained separately and used to predict the conditions through bootstrapping and bagging techniques. The program was able to predict the 13 conditions with an average accuracy of 96.95% and a micro-averaged F1 score of 0.91 on an independent test set, with the F1 score calculated to account for the presence of class imbalance. This program can potentially aid in enabling early disease prediction, facilitating timely treatment, and improving quality of life while lowering treatment costs and helping hospitals, emergency rooms, caregivers, and individuals with diagnosis predictions, prompt decision-making, and resource prioritization, especially in times of limited healthcare resources.
Risk prediction tools are becoming increasingly popular tools to assist in making clinical decisions. The models, however, are typically trained on data from general patient cohorts and may not be representative of and applicable to targeted patient cohorts when used in practice. This study overcame these obstacles by developing and evaluating a clinical risk prediction model using the MIMIC-III clinical dataset and an Artificial Neural Network (ANN). The suggested method accounts for all possible preprocessing steps—including normalization, encoding, managing missing values, and data balancing using SMOTE-ENN—to increase the dependability of the predictions. With an F1-Score (F1) of 96.9%, an accuracy (ACC) of 98.6%), a precision (PRE) of 97%, and a recall (REC) of 96.5%, the ANN model has high predictive capacity and can capture the complicated interaction between the clinical variables. The outcomes demonstrate that the suggested ANN architecture can accurately and consistently assess clinical risk, which in turn allows for the early identification of high-risk patients and aids healthcare providers in making data-informed therapeutic decisions. Because it can provide trustworthy prediction models from complex health data, the proposed method has great potential for clinical real-time application. It can also help develop smarter healthcare decision-support systems and enhance patient monitoring methods.
Shivani Jain· Journal of Artificial Intell...· 0 citations
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.
Early identification of heart disease is important to reduce mortality rates and to provide timely medical intervention for better patient outcomes. In recent studies, machine learning has been used to predict cardiovascular risk, but many existing models use basic feature sets and fixed decision rules. This can limit their ability to adapt when new data are introduced and may also reduce their capability to detect early signs of risk. In this study, we present a heart disease prediction framework that integrates acentric artificial intelligence (Ac-AI) with deep feature engineering to improve prediction accuracy. We adopted an autoencoder-based representation learning module to learn compact latent features from clinical data. These learned features were then combined with the original variables to provide a more informative set of inputs for subsequent analyses. The Ac-AI classifier applies cost-sensitive learning and an adaptive decision threshold to improve sensitivity for disease classes. Across multiple experiments conducted on a benchmark heart disease dataset, the proposed model showed performance comparable to that of random forest (RF), support vector machine (SVM), and XGBoost. In some runs, it performed better than these baseline models. Repeated independent runs provided evidence that the method produced consistent results across trials. These results suggest that Ac-AI combined with deep feature engineering can serve as a useful decision support framework for the early prediction of heart disease risk.
Sanad Aburass, Sunil Kumar, Himanshu Sharma et al.· Frontiers in Artificial Inte...· 0 citations
Heart disease is a serious threat to human health; it is one of the leading causes of death. Being able to predict it in advance can help doctors separate patients into different categories based on risk levels and provide the most needed care to those who require it most. One of the ways to predict it is using the machine learning method. However, it should be noted that there are some issues with such models, such as overfitting, noise sensitivity, and sometimes a relatively low predictive performance. To address these limitations, this proposed study aims to develop an ensemble learning-based framework for improved heart disease prediction using multiple datasets. In this study, different machine learning classification algorithms will be implemented and evaluated. Ensemble techniques, particularly voting and stacking, will be investigated by combining the predictions of multiple diverse base classifiers The proposed study will adopt the following procedure: data pre-processing, selection of predictive features, data normalization, model training, hyperparameter optimization, and performance evaluation. The following performance metrics will be used to assess the models’ performance: accuracy, precision, recall, F1-score, specificity, and ROC-AUC. The methods mentioned above will be used to test, analyze, and confirm the reliability of results. Moreover, we will apply Explainable Artificial Intelligence (XAI) to evaluate model performance in terms of the clinical features that are most relevant in predicting heart disease. We will use SHAP (SHapley Additive exPlanations) as XAI for interpreting the results. The proposed framework is expected to improve prediction accuracy and robustness compared the proposed study examines the effectiveness of ensemble learning and explainable artificial intelligence algorithms to predict heart disease risks and provide decision-making support to healthcare practitioners.
Choudhuri Saswat Pattnaik, Jyoti Upadhyaya, Durgeswari Sahu et al.· International Research Journ...· 0 citations
Cardiovascular diseases are the main reasons for death around the world at present, so early detection and intervention can be difficult. Review of Recent Applications of Machine Learning and Deep Learning in Cardiovascular Disease Prediction. Logistic regression, decision trees, random forests, support vector machines and gradient boosting have all been applied to the Cleveland and Kaggle cardiovascular datasets in previous studies. Based on research results, soft voting and stacking ensemble methods have been used to improve the prediction accuracy of a single classifier. Dense neural networks and hybrid Convolutional Neural Network - Long Short-Term Memory (CNN-LSTM) architectures are also deep learning models that have been researched and applied. Add to the above that SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) are popular methods for explaining Artificial Intelligence (AI) models. However, there are still many problems, such as an abundance of small public datasets, class imbalance, a lack of external validation, and poor clinical interpretability. The above are the problems of this paper, and some future directions for constructing a reliable cardiac disease prediction system with clinical applications are proposed.
Yi-Min Zhou· Applied and Computational En...· 0 citations
A multi-model framework using wearable sensor data to improve early disease risk prediction and support timely clinical decisions enables more robust and interpretable disease risk predictions and supports health monitoring, early clinical interventions as well as evidence-based clinical and healthcare decisions.
K. Manivannan· Journal of Intelligent Decis...· 0 citations
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