cs.AISep 4, 2026

A Hybrid Predictive Ensemble of Machine Learning and Deep Neural Networks for Early Cardiovascular Disease Risk Assessment

Authors: Balaji Venkateswaran

Organizations: Independent Researcher, Chennai, India

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Abstract

This study introduces an intelligent framework that integrates machine learning and deep neural network ensemble techniques for early detection and prognosis of cardiovascular diseases. The system utilizes real-time physiological data collected from Internet of Medical Things (IoMT) devices, including ECG sensors, heart rate monitors, and blood pressure trackers. To ensure the accuracy and reliability of input data, preprocessing steps such as noise reduction, normalization, and missing value imputation are employed. The most significant health indicators are identified through effective feature selection methods and then processed using optimized classifiers such as Support Vector Machines (SVM), Random Forests, and eXtreme Gradient Boosting (XGBoost), which are combined in an ensemble architecture to improve diagnostic precision. The framework demonstrates remarkable performance in predicting cardiovascular disease risk, achieving higher accuracy, reduced false positives, and enhanced consistency compared to conventional methods. It is designed on a cloud-based infrastructure that ensures scalability and real-time processing for continuous patient monitoring. Experimental evaluation on real-world cardiovascular datasets confirms the framework's efficiency in early-stage risk assessment and clinical decision support. The results highlight the potential of combining traditional machine learning and deep learning paradigms to achieve proactive healthcare management and improve patient outcomes.

Figures & tables

Figure 1: Process of heart disease prediction using IoT and machine learning.
Ref.Algorithm(s) usedDataset(s)Main features / contributions
[ 1 ]SVM, Logistic Regression, Decision TreeUCI Cleveland Heart Disease (UCI ML repo)Classic comparative study: feature engineering, cross-validation; identified top clinical predictors (cholesterol, resting BP, age); baseline for subsequent work.
[ 2 ]Random Forest, Gradient Boosting (XGBoost)Kaggle Heart Disease / UCI combined setsEmphasized feature importance and model interpretability; RF/XGBoost achieved higher AUC than single linear models; used SMOTE for class imbalance.
[ 3 , 24 ]CNN on raw ECG segmentsMIT-BIH Arrhythmia Database / PhysioNet ECGEnd-to-end deep learning on ECG waveforms for arrhythmia detection; minimal hand-crafted features; robust to noise after augmentation.
[ 4 ]LSTM / CNN–LSTM hybridPhysioNet / MIMIC-III (ICU signals)Temporal modeling of sequential vitals and ECG for early warning; improved early event detection vs. static models.
[ 5 ]Autoencoder + SVM (feature extraction + classifier)PTB Diagnostic ECG DatabaseUnsupervised feature learning with denoising autoencoders to extract salient ECG features, then SVM for classification; reduced dimensionality and noise sensitivity.
[ 6 ]Stacking ensemble (SVM, RF, XGBoost)Framingham Risk Study subset / UCI variantsStacked meta-learner combining heterogeneous base models; improved calibration and reduced false positives for 5-year risk prediction.
Table 1: Review of literature for machine and deep learning based heart disease prediction methods.
AttributeTypeDescriptionPossible values / rangeImportance / role
AgeNumericalAge of the patient in years29–77Predictor of cardiovascular risk; older age often correlates with higher risk
SexCategoricalGender of the patient0 = Female, 1 = MaleUsed to analyze gender-specific heart disease prevalence
Chest Pain Type (cp)CategoricalType of chest pain experienced0 = Typical Angina, 1 = Atypical Angina, 2 = Non-anginal Pain, 3 = AsymptomaticStrong indicator of heart disease; affects model classification
Resting Blood Pressure (trestbps)NumericalBlood pressure at rest in mmHg94–200High BP is a known risk factor for CVD
Cholesterol (chol)NumericalSerum cholesterol in mg/dl126–564Predictor for atherosclerosis and cardiovascular risk
Fasting Blood Sugar (fbs)CategoricalFasting blood sugar >> 120 mg/dl0 = False, 1 = TrueIndicates diabetes risk; contributes to cardiovascular risk
Table 2: Description of the Kaggle Heart Disease Dataset including features, types, and clinical significance.
Figure 2: Proposed research methodology.
Figure 3: Overall evaluation of heart disease prediction algorithms.
Figure 4: Accuracy-based evaluation of heart disease prediction algorithms.
Figure 5: Precision-based evaluation of heart disease prediction algorithms.
Figure 6: Recall-based evaluation of heart disease prediction algorithms.
Figure 7: F1-score-based evaluation of heart disease prediction algorithms.
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