Abstract
Coronary Heart Disease (CHD) is a significant public health concern, posing a substantial burden on individuals, families and healthcare systems, necessitating accurate prediction and timely diagnosis for effective management. This research paper presents a data-driven approach for enhancing CHD prediction using meticulous preprocessing to address null values, ensuring data integrity, dataset resampling to address imbalance and Min-Max normalization which further enhances the comparability of features for the Framingham CHD dataset. A Stacking Ensemble Model with Random Forest as the meta classifier is proposed, that combines the predictive capabilities of the base classifiers including Logistic Regression, KNN, SVM, Decision Tree and XGBoost. The proposed approach yields exceptional results with an accuracy of 97.39% in CHD prediction. This study highlights the pivotal role of data preprocessing and ensemble modeling which showcases their potential for improving CHD research and healthcare practices.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gupta, A., Kumar, R., Arora, H.S., Raman, B.: C-CADZ: computational intelligence system for coronary artery disease detection using Z-Alizadeh Sani dataset. Appl. Intell. 52(3), 2436–2464 (2022). https://doi.org/10.1007/s10489-021-02467-3
El-Shafiey, M.G., Hagag, A., El-Dahshan, E.-S.A., Ismail, M.A.: A hybrid GA and PSO optimized approach for heart-disease prediction based on random forest. Multimed. Tools Appl. 81(13), 18155–18179 (2022). https://doi.org/10.1007/s11042-022-12425-x
Chaurasia, V., Chaurasia, A.: Novel method of characterization of heart disease prediction using sequential feature selection-based ensemble technique. Biomed. Mater. Devices (2023). https://doi.org/10.1007/s44174-022-00060-x
Diwan, S., Thakur, G., Sahu, S., Sahu, M., Swamy, N.K.: Predicting heart diseases through feature selection and ensemble classifiers. J. Phys. Conf. Ser. 2273, 012027 (2022). https://doi.org/10.1088/1742-6596/2273/1/012027
Nagavelli, U., Samanta, D., Chakraborty, P.: Machine learning technology-based heart disease detection models. J. Healthc. Eng. 2022, e7351061 (2022). https://doi.org/10.1155/2022/7351061
Abdar, M., Książek, W., Acharya, U.R., Tan, R.-S., Makarenkov, V., Pławiak, P.: A new machine learning technique for an accurate diagnosis of coronary artery disease. Comput. Methods Programs Biomed. 179, 104992 (2019). https://doi.org/10.1016/j.cmpb.2019.104992
Nagarajan, S.M., Muthukumaran, V., Murugesan, R., Joseph, R.B., Munirathanam, M.: Feature selection model for healthcare analysis and classification using classifier ensemble technique. Int. J. Syst. Assur. Eng. Manag. (2021). https://doi.org/10.1007/s13198-021-01126-7
Louridi, N., Douzi, S., El Ouahidi, B.: Machine learning-based identification of patients with a cardiovascular defect. J. Big Data 8(1), 133 (2021). https://doi.org/10.1186/s40537-021-00524-9
Mohapatra, S., et al.: A stacking classifiers model for detecting heart irregularities and predicting Cardiovascular Disease. Healthc. Anal. 3, 100133 (2023). https://doi.org/10.1016/j.health.2022.100133
Ghasemieh, A., Lloyed, A., Bahrami, P., Vajar, P., Kashef, R.: A novel machine learning model with Stacking Ensemble Learner for predicting emergency readmission of heart-disease patients. Decis. Anal. J. 7, 100242 (2023). https://doi.org/10.1016/j.dajour.2023.100242
Khan, Y.F., Kaushik, B., Chowdhary, C.L., Srivastava, G.: Ensemble model for diagnostic classification of Alzheimer’s disease based on brain anatomical magnetic resonance imaging. Diagn. Basel Switz. 12(12), 3193 (2022). https://doi.org/10.3390/diagnostics12123193
Mahmood, S.S., Levy, D., Vasan, R.S., Wang, T.J.: The framingham heart study and the epidemiology of cardiovascular diseases: a historical perspective. Lancet 383(9921), 999–1008 (2014). https://doi.org/10.1016/S0140-6736(13)61752-3
Framingham. https://www.kaggle.com/datasets/eeshanpaul/framingham. Accessed 17 Aug 2023
Khan, Y.F., Kaushik, B., Rahmani, M.K.I., Ahmed, M.E.: Stacked deep dense neural network model to predict Alzheimer’s dementia using audio transcript data. IEEE Access 10, 32750–32765 (2022). https://doi.org/10.1109/ACCESS.2022.3161749
Chadha, A., Kaushik, B.: A survey on prediction of suicidal ideation using machine and ensemble learning. Comput. J. 64(11), 1617–1632 (2021). https://doi.org/10.1093/comjnl/bxz120
Bharti, R., Khamparia, A., Shabaz, M., Dhiman, G., Pande, S., Singh, P.: Prediction of heart disease using a combination of machine learning and deep learning. Comput. Intell. Neurosci. 2021, e8387680 (2021). https://doi.org/10.1155/2021/8387680
Chadha, A., Kaushik, B.: Performance evaluation of learning models for identification of suicidal thoughts. Comput. J. 65(1), 139–154 (2022). https://doi.org/10.1093/comjnl/bxab060
Diwakar, M., Tripathi, A., Joshi, K., Memoria, M., Singh, P., Kumar, N.: Latest trends on heart disease prediction using machine learning and image fusion. Mater. Today Proc. 37, 3213–3218 (2021). https://doi.org/10.1016/j.matpr.2020.09.078
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Mahajan, A., Kaushik, B. (2024). A Data Preprocessing and Stacking Ensemble Learning Model for Improved CHD Prediction. In: Singh, J., Anastassiou, G.A., Baleanu, D., Kumar, D. (eds) Advances in Mathematical Modelling, Applied Analysis and Computation . ICMMAAC 2023. Lecture Notes in Networks and Systems, vol 953. Springer, Cham. https://doi.org/10.1007/978-3-031-56304-1_16
Download citation
DOI: https://doi.org/10.1007/978-3-031-56304-1_16
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-56303-4
Online ISBN: 978-3-031-56304-1
eBook Packages: EngineeringEngineering (R0)