Enhancing Prediction Accuracy for Cardiovascular Heart Diseases through Optimized Ensembled Classifiers

International Journal of Electronics and Communication Engineering

© 2026 by SSRG - IJECE Journal

Volume 13 Issue 3

Year of Publication : 2026

Authors : Pratikkumar Parmar, Nirali Shah, Vishal P. Jariwala

10.14445/23488549/IJECE-V13I3P117

How to Cite?

Pratikkumar Parmar, Nirali Shah, Vishal P. Jariwala, "Enhancing Prediction Accuracy for Cardiovascular Heart Diseases through Optimized Ensembled Classifiers," SSRG International Journal of Electronics and Communication Engineering, vol. 13,  no. 3, pp. 203-212, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I3P117

Abstract:

Cardiovascular Diseases (CVD) rank among the leading causes of global mortality. However, detecting CVD early and accurately can considerably reduce the rates of fatality. In order to support early diagnosis and intervention in cardiovascular health, Machine Learning (ML) models have arisen as powerful tools. A robust predictive system for cardiovascular heart disease using a suite of ML-based ensembled algorithms is presented in this study. A comprehensive heart disease dataset from IEEE DataPort is used to train the prediction models. The dataset is preprocessed by different processes, including data cleaning, standardization, and data balancing using the Synthetic Minority Oversampling Technique (SMOTE). This preprocessed dataset is bisected into a training dataset and a testing dataset. The ensembled machine learning algorithms, including AdaBoost (AdB), Gradient Boosting (GB), XGBoost (XGB), Extra Tree Classifier (ET), and Random Forest (RF), are trained using the training dataset for the prediction process. Hyperparameters of these algorithms are tuned using RandomSearchCV with 5-fold cross-validation to optimize the classifier algorithms and improve the efficiency of the prediction process. Standard performance parameters, accuracy, recall, precision, F1-score, and ROC-AUC, are used for the evaluation of the performance of each optimized model and compared with state-of-the-art model performances without optimization. Among all models, the optimized RF classifier demonstrated superior performance, achieving the highest accuracy of 96.31%, outperforming all other ensembled classifiers.

Keywords:

CVD, Ensembled, Bagging, Boosting, SMOTE, RandomSearchCV.

References:

[1] Ankit Kumar, Kamred Udham Singh, and Manish Kumar, “A Clinical Data Analysis Based Diagnostic Systems for Heart Disease Prediction Using Ensemble Method,” Big Data Mining and Analytics, vol. 6, no. 4, pp. 513-525, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Daniyal Asif et al., “Enhancing Heart Disease Prediction through Ensemble Learning Techniques with Hyperparameter Optimization,” Algorithms, vol. 16, no. 6, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Annwesha Banerjee Majumder et al., “Heart Disease Prediction Using Concatenated Hybrid Ensemble Classifiers,” Algorithms, vol. 16, no. 12, pp. 1-19, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Shahid Mohammad Ganie et al., “An Improved Ensemble Learning Approach for Heart Disease Prediction Using Boosting Algorithms,” Computer Systems Science and Engineering, vol. 46, no. 3, pp. 3993-4006, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Xiao-Yan Gao et al., “Improving the Accuracy for Analyzing Heart Diseases Prediction Based on the Ensemble Method,” Complexity, vol. 2021, no. 1, pp. 1-10, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Joshua Emakhu, Sujeet Shrestha, and Suzan Arslanturk, “Prediction System for Heart Disease Based on Ensemble Classifiers Prediction System for Heart Disease Based on Ensemble Classifiers,” Proceedings of the 5th NA International Conference on Industrial Engineering and Operations Management Detroit, Michigan, USA, pp. 2337-2347, 2020.
[Google Scholar] [Publisher Link]
[7] Najmu Nissa, Sanjay Jamwal, and Mehdi Neshat, “A Technical Comparative Heart Disease Prediction Framework Using Boosting Ensemble Techniques,” Computation, vol. 12, no. 1, pp. 1-22, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Deepali Yewale, S.P. Vijayaragavan, and V.K. Bairagi, “An Effective Heart Disease Prediction Framework based on Ensemble Techniques in Machine Learning,” International Journal of Advanced Computer Science and Applications, vol. 14, no. 2, pp. 182-190, 2023.
[Google Scholar] [Publisher Link]
[9] Mohammed Amine Bouqentar et al., “Early Heart Disease Prediction using Feature Engineering and Machine Learning Algorithms,” Heliyon, vol. 10, no. 19, pp. 1-23, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Sohaib Asif et al., “Improving the Accuracy of Diagnosing and Predicting Coronary Heart Disease using Ensemble Method and Feature Selection Techniques,” Cluster Computing, vol. 27, pp. 1927-1946, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Agung Muliawan, Achmad Rizal, and Sugondo Hadiyoso, “Heart Disease Prediction based on Physiological Parameters Using Ensemble Classifier and Parameter Optimization,” Journal of Applied Engineering and Technological Science, vol. 5, no. 1, pp. 258-267, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Enoch Sakyi-Yeboah et al., “Heart Disease Prediction Using Ensemble Tree Algorithms: A Supervised Learning Perspective,” Applied Computational Intelligence and Soft Computing, vol. 2025, no. 1, pp. 1-18, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Ahmed Almulihi et al., “Ensemble Learning Based on Hybrid Deep Learning Model for Heart Disease Early Prediction,” Diagnostics, vol. 12, no. 12, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Sarria E. A. Ashri, M. M. El-Gayar, and Eman M. El-Daydamony, “HDPF: Heart Disease Prediction Framework Based on Hybrid Classifiers and Genetic Algorithm,” IEEE Access, vol. 9, pp. 146797-146809, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Mohammed Ahmed, and Idress Husien, “Hybrid Machine Learning Approach for Accurate Heart Disease Prediction,” International Journal of Intelligent Engineering and Systems, vol. 17, no. 4, pp. 728-737, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Priyanka Gupta, and D.D. Seth, “Improving the Prediction of Heart Disease Using Ensemble Learning and Feature Selection,” International Journal of Advances in Soft Computing and its Applications, vol. 14, no. 2, pp. 37-48, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Abdullah Alqahtani et al., “Cardiovascular Disease Detection using Ensemble Learning,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, pp. 1-9, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Asfandyar Khan et al., “Cardiovascular and Diabetes Diseases Classification using Ensemble Stacking Classifiers with SVM as a Meta Classifier,” Diagnostics, vol. 12, no. 11, pp. 1-15, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Sushree Chinmayee Patra, B. Uma Maheswari, and Peeta Basa Pati, “Forecasting Coronary Heart Disease Risk with a 2-Step Hybrid Ensemble Learning Method and Forward Feature Selection Algorithm,” IEEE Access, vol. 11, pp. 136758-136769, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Khomkrit Yongcharoenchaiyasit et al., “Gradient Boosting Based Model for Elderly Heart Failure, Aortic Stenosis, and Dementia Classification,” IEEE Access, vol. 11, pp. 48677-48696, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[21] G. Manikandan et al., “Classification Models Combined with Boruta Feature Selection for Heart Disease Prediction,” Informatics in Medicine Unlocked, vol. 44, pp. 1-12, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[22] Sherko H. Murad et al., “Hybrid Genetic Algorithms-Driven Optimization of Machine Learning Models for Heart Disease Prediction,” MethodsX, vol. 15, pp. 1-10, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Mana Saleh Al Reshan et al., “A Robust Heart Disease Prediction System using Hybrid Deep Neural Networks,” IEEE Access, vol. 11, pp. 121574-121591, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Nadikatla Chandrasekhar, and Samineni Peddakrishna, “Enhancing Heart Disease Prediction Accuracy Through Machine Learning Techniques and Optimization,” Processes, vol. 11, no. 4, pp. 1-31, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Kaushalya Dissanayake, and Gapar Md Johar, “Comparative Study on Heart Disease Prediction using Feature Selection Techniques on Classification Algorithms,” Applied Computational Intelligence and Soft Computing, vol. 2021, no. 1, pp. 1-17, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Norma Latif Fitriyani et al., “HDPM: An Effective Heart Disease Prediction Model for a Clinical Decision Support System,” IEEE Access, vol. 8, pp. 133034-133050, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Aqsa Rahim et al., “An Integrated Machine Learning Framework for Effective Prediction of Cardiovascular Diseases,” IEEE Access, vol. 9, pp. 106575-106588, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Ayesha Noor et al., “Heart Disease Prediction using Stacking Model with Balancing Techniques and Dimensionality Reduction,” IEEE Access, vol. 11, pp. 116026-116045, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Jian Ping Li et al., “Heart Disease Identification Method using Machine Learning Classification in E-Healthcare,” IEEE Access, vol. 8, pp. 107562-107582, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Parmonangan R. Togatorop et al., “Optimizing Random Forest using Genetic Algorithm for Heart Disease Classification,” Lontar Komputer: Scientific Journal of Information Technology, vol. 13, no. 1, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[31] Mirza Muntasir Nishat et al., “A Comprehensive Investigation of the Performances of Different Machine Learning Classifiers with SMOTE-ENN Oversampling Technique and Hyperparameter Optimization for Imbalanced Heart Failure Dataset,” Scientific Programming, vol. 2022, no. 1, pp. 1-17, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Abbas Jafar, and Myungho Lee, “HypGB: High Accuracy GB Classifier for Predicting Heart Disease with HyperOpt HPO Framework and LASSO FS Method,” IEEE Access, vol. 11, pp. 138201-138214, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[33] Achyut Tiwari, Aryan Chugh, and Aman Sharma, “Ensemble Framework for Cardiovascular Disease Prediction,” Computers in Biology and Medicine, vol. 146, 2022.
[CrossRef] [Google Scholar] [Publisher Link]