International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 03 | Mar 2026
p-ISSN: 2395-0072
www.irjet.net
Cardiovascular Disease Prediction: An Ensemble Machine Learning Approach 1Kunal Patil,2Nimesh Patil,3Pratik Sirsath,4Sanjay Sonkawade, 5Anand Ingle 1234 B.E Student, MGM college of Engineering and Technology 5 Professor, MGM college of Engineering and Technology
---------------------------------------------------------------------***--------------------------------------------------------------------Abstract— cardiovascular disease (CVD) is one of the major causes of morbidity and mortality worldwide, posing burden on healthcare systems. Accurate and early prediction of cardiovascular disease is critical for enabling preventive measures with the increasing availability of medical data, machine learning techniques have emerged as effective tools for disease prediction and risk assessment. This paper presents an ensemble machine learning approach for predicting cardiovascular disease using a combination of clinical, demographic, and lifestyle-related features such as age, gender, blood pressure, cholesterol levels, body mass index, and smoking status. There are some Several individual classification models, such as Logistic Regression, Decision Tree, Support Vector Machine, and Random Forest, are developed and evaluated. To improve prediction performance and reduce model variance, ensemble techniques such as majority voting and boosting are employed to integrate the outputs of multiple base learners. The proposed ensemble model is assessed using standard evaluation metrics including accuracy, precision, recall, F1-score, and area under the ROC curve (AUC). Experimental results shows that the ensemblebased model achieves superior performance compared to another classifiers, offering better robustness and generalization capability. The proposed approach can assist healthcare professionals in early diagnosis, risk stratification, and decision support, ultimately contributing to the reduction of cardiovascular diseaserelated complications.
rise in cardiovascular diseases. So the Early detection and accurate prediction of CVD play a vital role in reducing mortality rates and improving patient outcomes through timely medical and lifestyle modifications. Traditional methods for diagnosing cardiovascular disease mostly depends on clinical expertise, medical examinations, and laboratory tests. While these methods are effective, But they can be time-consuming, costly, and subject to human mistakes. This rising volume of healthcare data generated from electronic health records, wearable devices, and diagnostic systems has made it increasingly difficult for healthcare professionals to manually analyse and interpret data efficiently. This has created a strong demand for automated and intelligent systems capable of assisting doctors in disease prediction and decision-making about the disease. Machine learning (ML) has emerged as a powerful tool in the healthcare domain due to its ability to analyse complex datasets, identify hidden patterns, and make accurate and quicker predictions. Machine learning algorithms such as Logistic Regression, Decision Trees, Support Vector Machines (SVM), k-Nearest Neighbours (k-NN), and Random Forest have been widely applied for cardiovascular disease prediction. These models can process number of risk factors simultaneously and provide predictive insights that support early diagnosis. However, the performance of individual models often depends on the behaviour of the dataset, feature distribution, and algorithm-specific assumptions, which may lead to limited accuracy or generalization issues.
1 INTRODUCTION Cardiovascular disease (CVD) is a disease which comes from group of disorders affecting the heart and blood vessels and remains one of the major causes of death worldwide. According to global statistics, millions of people die each year due to heart-related problems such as coronary artery disease, heart failure, stroke, and hypertension. The increasing prevalence of uneven lifestyles, unhealthy dietary habits, smoking, obesity, diabetes, and stress has further contributed to the faster
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