A novel lightweight deep learning model for early prediction of cardiovascular disease

Cardiovascular Disease (CVD) is still the main cause of death globally. Hence, to have timely clinical intervention, there is a need for the prediction of early risk models which are accurate and dependable. Here is a lightweight and strong artificial intelligence-based system for the early prediction of CVD using clinical data. We have created an end-to-end method comprising data preprocessing, stratified train and test splitting, and class imbalance treatment by SMOTE exclusively on the training set to ensure that there is no data leakage. We evaluate a series of machine learning (ML) and deep learning (DL) models, including Logistic Regression (LR), Decision Tree (DT), K-Nearest Neighbors (KNN), Random Forest (RF), Support Vector Machine (SVM), and various Artificial Neural Network (ANN) architectures, systematically. In order to check the robustness and generalizability of the models proposed, we conduct a k-fold cross-validation procedure and the performance measures are reported using evaluation matrices such as accuracy, precision, recall, F1-score, and ROC curve along with statistical metrics (mean and standard deviation). The results from the experiments show that the fine-tuned four-layer ANN yields the best prediction performance. Nevertheless, and in contrast to conventional studies, evaluation of the models is strongly based on robust validation and statistical reliability and not only on accuracy.