Predictive Modeling for Early Detection and Risk Assessment of Cardiovascular Diseases Using the Ensemble Stacked Neural Network Model

Abstract

Medical experts feel difficulty in making the right decision about cardiac arrest. Early detection of cardiovascular disease means a better chance of survival for the patient otherwise it can lead to death. This study proposes a state-of-the-art model named Ensemble Stacked Neural Network (ESNN) that combines both machine learning (ML) and deep learning (DL) techniques for early detection and risk assessment of cardiac disease. Our model pools multiple widely recognized cardiac disease datasets (Cleveland, Hungarian, Switzerland, Long Beach VA, and Statlog) to create a comprehensive dataset for training and evaluation. The ESNN model begins with extensive data pre-processing, including the elimination of null values, outliers, and duplicates, followed by Z-score normalization to standardize the feature scales. We address class imbalance using the Randomized Oversampling technique. Principal Component Analysis (PCA) is applied for feature reduction, ensuring the most informative components are retained. We employed a diverse set of nine ML algorithms, consisting of XGBoost and naïve Bayes, achieving individual accuracies ranging from 69% to 89%. ESNN model integrates a neural network that is trained and validated on the processed data, producing predictions that serve as additional features for the subsequent ML models. The chosen nine classifiers are used as base models and these are tuned using GridSearchCV for optimal Hyperparameters. The base models consist of a DT, RF, LR, gradient boosting, XGBoost, AdaBoost, SVM, KNN, and NB. RF is used as a meta-model in a stacking ensemble framework. The proposed model (ESNN) was carefully trained and tested and achieved an accuracy of 95%. This integration of machine learning and deep neural network methods within the ESNN framework establishes strong predictive modelling. It provides a reliable tool for cardiologists to diagnose heart disease risk.