KNN IMPUTATION AND HARD VOTING ENSEMBLE FOR HEART DISEASE PREDICTION
Keywords:
Heart disease prediction, machine learning, K-nearest neighbour imputation, hard voting ensemble, IEEE Dataport, classificationAbstract
Heart disease (HD) remains one of the leading causes of morbidity and
Accepted Date: 24/07/2026 Published Date: 10/08/2026 Type: Research
mortality worldwide, posing a significant public health challenge. Early and accurate identification of individuals at risk is essential for reducing disease progression, preventing complications, and improving patient outcomes. Machine learning (ML) has emerged as an effective approach for medical diagnosis by identifying complex patterns within clinical data and supporting evidence-based clinical decision-making. Consequently, numerous ML algorithms have been applied to cardiovascular disease prediction to enhance the accuracy and reliability of conventional diagnostic methods. However, limited research has examined the combined application of K-nearest neighbour (KNN) data imputation and a hard voting ensemble (HVE) model for HD prediction using the HD dataset from IEEE Dataport. This study evaluates the effectiveness of KNN data imputation and a HVE model in improving the predictive performance of HD classification. The study utilized 1,190 patient records from the IEEE Dataport HD dataset. Following data preprocessing, the dataset was divided into training and testing sets using an 80:20 ratio, resulting in 885 training samples and 238 testing samples. Four widely used classifiers, namely Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), were trained and evaluated. A HVE model was subsequently developed by combining the predictions of the individual classifiers to improve overall classification performance. The proposed framework achieved a classification accuracy of 93.70%, demonstrating that the HVE effectively enhanced predictive performance while reducing model bias. Furthermore, KNN data imputation effectively addressed missing values, contributing to improved classification accuracy and model robustness. Overall, the proposed framework provides a reliable approach for HD prediction and offers practical support for early diagnosis, clinical decision-making, and preventive healthcare.
References
Akkur, E. (2023). Prediction of Cardiovascular Disease Based on Voting Ensemble Model and SHAP Analysis. Sakarya University Journal of Computer and Information Sciences, 6(3), 226–238. https://doi.org/10.35377/saucis...1367326
Altamimi, A., Alarfaj, A. A., Umer, M., Alabdulqader, E. A., Alsubai, S., Kim, T. H., & Ashraf,
I. (2024). An automated approach to predict diabetic patients using KNN imputation and effective data mining techniques. BMC Medical Research Methodology, 24(1). https://doi.org/10.1186/s12874-024-02324-0
Batista, G. E. A. P. A., & Monard, M. C. (2003). An analysis of four missing data treatment methods for supervised learning. Applied Artificial Intelligence, 17(5–6), 519–533. https://doi.org/10.1080/713827181
Behera, M. P., Sarangi, A., Mishra, D., & Sarangi, S. K. (2022). A Hybrid Machine Learning algorithm for Heart and Liver Disease Prediction Using Modified Particle Swarm Optimization with Support Vector Machine. Procedia Computer Science, 218, 818–827. https://doi.org/10.1016/j.procs.2023.01.062
Budholiya, K., Shrivastava, S. K., & Sharma, V. (2022). An optimized XGBoost based diagnostic system for effective prediction of heart disease. Journal of King Saud University - Computer and Information Sciences, 34(7), 4514–4523. https://doi.org/10.1016/j.jksuci.2020.10.013
Chandrasekhar, N., & Peddakrishna, S. (2023). Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization. Processes, 11(4). https://doi.org/10.3390/pr11041210
Doppala, B. P., Bhattacharyya, D., Janarthanan, M., & Baik, N. (2022). A Reliable Machine Intelligence Model for Accurate Identification of Cardiovascular Diseases Using Ensemble Techniques. Journal of Healthcare Engineering, 2022. https://doi.org/10.1155/2022/2585235
Dubey, M., Tembhurne, J., & Makhijani, R. (2024). Heart Failure prediction on diversified datasets to improve generalizability using 2-Level Stacking. Multidisciplinary Science Journal, 6(3). https://doi.org/10.31893/multiscience.2024028
Ghasemieh, A., Lloyed, A., Bahrami, P., Vajar, P., & Kashef, R. (2023). A novel machine learning model with Stacking Ensemble Learner for predicting emergency readmission of heart-disease patients. Decision Analytics Journal, 7.
https://doi.org/10.1016/j.dajour.2023.100242
Gour, S., Randa, R., Pandagre, K. N., Neema, A., Jat, S., & Malakar, D. (2024.). A Comprehensive Review on Predicting Heart Failure with Ensemble Learning: Integrating Environmental Variables and Health Perspectives. In International Journal of Environmental Sciences (Vol. 11). https://www.theaspd.com/ijes.php
Joel, L. O., Doorsamy, W., & Paul, B. S. (2025). A comparative study of imputation techniques for missing values in healthcare diagnostic datasets. International Journal of Data Science and Analytics, 20(7), 6357–6373. https://doi.org/10.1007/s41060-025-00825-9
Kadhim, M. A., & Radhi, A. M. (2023). Heart disease classification using optimized Machine learning algorithms. Iraqi Journal for Computer Science and Mathematics, 4(2), 31–42. https://doi.org/10.52866/ijcsm.2023.02.02.004
Kalla, D., & Chandrasekaran, A. (2023). HEART DISEASE PREDICTION USING
MACHINE LEARNING AND DEEP LEARNING. International Journal of Data Mining & Knowledge Management Process, 13(03), 1–14. https://doi.org/10.5121/ijdkp.2023.13301
Liu, J., Dong, X., Zhao, H., & Tian, Y. (2022). Predictive Classifier for Cardiovascular Disease Based on Stacking Model Fusion. Processes, 10(4). https://doi.org/10.3390/pr10040749
Magboo, V. P. C., & Magboo, M. S. A. (2023). Cardiovascular disease prediction with imputation techniques and recursive feature elimination. AIP Conference Proceedings, 2602. https://doi.org/10.1063/5.0124079
Mahmud, I., Kabir, M. M., Mridha, M. F., Alfarhood, S., Safran, M., & Che, D. (2023). Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel. Diagnostics, 13(15). https://doi.org/10.3390/diagnostics13152540
Maidul Islam, M., Nasrin Tania, T., Akter, S., & Hassan Shakib, K. (2024). An Improved Heart Disease Prediction Using Stacked Ensemble Method. Telecommunications Engineering, vol 490. Springer, Cham. https://doi.org/10.1007/978-3-031-34619-4_8
Ozcan, M., & Peker, S. (2023). A classification and regression tree algorithm for heart disease modeling and prediction. Healthcare Analytics, 3. https://doi.org/10.1016/j.health.2022.100130
Rustam, F., Ishaq, A., Munir, K., Almutairi, M., Aslam, N., & Ashraf, I. (2022).
Incorporating CNN Features for Optimizing Performance of Ensemble Classifier for Cardiovascular Disease Prediction. Diagnostics, 12(6). https://doi.org/10.3390/diagnostics12061474
Sasu, G. V., Ciubotaru, B. I., Goga, N., & Vasilățeanu, A. (2025). Addressing Missing Data Challenges in Geriatric Health Monitoring: A Study of Statistical and Machine Learning Imputation Methods. Sensors, 25(3). https://doi.org/10.3390/s25030614
Shareef, A. Q., & Kurnaz, S. (2023). Deep Learning Based COVID-19 Detection via Hard Voting Ensemble Method. Wireless Personal Communications. https://doi.org/10.1007/s11277-023-10485-2
Siddhartha, M. (2020). Heart disease dataset (comprehensive) [Data set]. IEEE Dataport. https://ieee-dataport.org/open-access/heart-disease-dataset-comprehensive.
Shaheenur Islam Sumon, M., Sakib Bin Islam, M., Sohanur Rahman, M., Sakib Abrar Hossain, M., Khandakar, A., Hasan, A., Murugappan, M., & H Chowdhury, M. E. (n.d.). CardioTabNet: A Novel Hybrid Transformer Model for Heart Disease Prediction using Tabular Medical Data.
Tezerjan, M. Y., & Mollabagher, M. (2026). From Data to Treatment: Early Detection of Heart Disease Using Machine Learning Techniques. In Journal of Ilam University of Medical Sciences (Vol. 34, Number 1).
Umar, N., Hassan, S. K., Umar, A., & Ahmed, S. S. (2025). Predicting Heart Diseases by Selective Machine Learning Algorithms. Journal of Applied Sciences and Environmental Management, 29(1), 255–261. https://doi.org/10.4314/jasem.v29i1.32
Wazid, M., Singh, J., Das, A. K., Shetty, S., Khan, M. K., & Rodrigues, J. J. P. C. (2022). ASCP-IoMT: AI-Enabled Lightweight Secure Communication Protocol for Internet of Medical Things. IEEE Access, 10, 57990–58004. https://doi.org/10.1109/ACCESS.2022.3179418
Zhang, S. (2012). Nearest neighbour selection for iteratively kNN imputation. Journal of Systems and Software, 85(11), 2541–2552. https://doi.org/10.1016/j.jss.2012.05.073
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