Heart Disease Prediction Using a Hybrid Random Forest–Artificial Neural Network Model: A Replication and Extension of the HRFLM Approach

References

[1] S. Mohan, C. Thirumalai, and G. Srivastava, "Effective heart disease prediction using hybrid machine learning techniques," IEEE Access, vol. 7, pp. 81542-81554, 2019.

[2] D. Asif, M. Bibi, M. S. Arif, and A. Mukheimer, "Enhancing heart disease prediction through ensemble learning techniques with hyperparameter optimization," Algorithms, vol. 16, no. 6, article 308, 2023.

[3] A. K. Garate-Escamila, A. H. El Hassani, and E. Andres, "Classification models for heart disease prediction using feature selection and PCA," Informatics in Medicine Unlocked, vol. 19, article 100330, 2020.

[4] S. I. Ayon, M. M. Islam, and M. R. Hossain, "Coronary artery heart disease prediction: A comparative study of computational intelligence techniques," IETE Journal of Research, vol. 68, pp. 2488-2507, 2020.

[5] U. Nagavelli, D. Samanta, and P. Chakraborty, "Machine learning technology-based heart disease detection models," Journal of Healthcare Engineering, vol. 2022, article 7351061, 2022.

[6] R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, and P. Singh, "Prediction of heart disease using a combination of machine learning and deep learning," Computational Intelligence and Neuroscience, vol. 2021, article 8387680, 2021.

[7] R. Katarya and S. K. Meena, "Machine learning techniques for heart disease prediction: A comparative study and analysis," Health and Technology, vol. 11, no. 1, pp. 87-97, 2021.

[8] N. A. Baghdadi, S. M. F. Abdelaliem, A. Malki, I. Gad, A. Ewis, and E. Atlam, "Advanced machine learning techniques for cardiovascular disease early detection and diagnosis," Journal of Big Data, vol. 10, article 144, 2023.

[9] S. M. Ganie, P. K. D. Pramanik, and Z. Zhao, "Ensemble learning with explainable AI for improved heart disease prediction based on multiple datasets," Scientific Reports, 2025.

[10] I. D. Mienye and N. Jere, "Optimized ensemble learning approach with explainable AI for improved heart disease prediction," Information, vol. 15, no. 7, article 394, 2024.

[11] P. Rani, R. Kumar, N. M. O. S. Ahmed, and A. Jain, "A decision support system for heart disease prediction based upon machine learning," Journal of Reliable Intelligent Environments, vol. 7, no. 3, pp. 263-275, 2021.

[12] N. L. Fitriyani, M. Syafrudin, G. Alfian, and J. Rhee, "HDPM: An effective heart disease prediction model for a clinical decision support system," IEEE Access, vol. 8, pp. 133034-133050, 2020.

[13] C. M. Bhatt, P. Patel, T. Ghetia, and P. L. Mazzeo, "Effective heart disease prediction using machine learning techniques," Algorithms, vol. 16, no. 2, article 88, 2023.

[14] P. Ghosh et al., "Efficient prediction of cardiovascular disease using machine learning algorithms with Relief and LASSO feature selection techniques," IEEE Access, vol. 9, pp. 19304-19326, 2021.

[15] A. Saboor et al., "A method for improving prediction of human heart disease using machine learning algorithms," Mobile Information Systems, vol. 2022, article 1410169, 2022.

[16] E. Dritsas and M. Trigka, "Efficient data-driven machine learning models for cardiovascular diseases risk prediction," Sensors, vol. 23, no. 3, article 1161, 2023.

[17] M. Swathy and K. Saruladha, "A comparative study of classification and prediction of cardiovascular diseases using machine learning and deep learning techniques," ICT Express, vol. 8, no. 1, pp. 109-116, 2022.

[18] M. Bhushan, A. Pandit, and A. Garg, "Machine learning and deep learning techniques for the analysis of heart disease: A systematic literature review, open challenges and future directions," Artificial Intelligence Review, 2023.

@article{1720179,
      author = {Jyoti, Amandeep, Dharmender Kumar, Ankit, Suraj Sharma},
      title = {Heart Disease Prediction Using a Hybrid Random Forest–Artificial Neural Network Model: A Replication and Extension of the HRFLM Approach},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {1},
      pages = {2784-2792},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1720179.pdf},
      abstract = {The leading cause of death worldwide is cardiovascular disease, which is motivating screening using non-invasive machine learning on clinical parameters collected routinely. The present work paper reviews eight studies representative and heart disease prediction most relevant, highlights the major research gaps they leave open, and replicates a well-known hybrid baseline the Hybrid Random Forest with Linear Model (HRFLM) proposed by Mohan et al., and finally presents an improved Hybrid RF-ANN. The proposed model rescaling clinical input feature by their importance score derived from Random-Forest and feeding those features to two hidden layers NN. The Hybrid RF-ANN, whose code is published, attained an accuracy of 90.16% in the study, a significant 9.83 percentage point improvement over the accuracy of HRFLM, while the Hybrid RF-ANN achieved the highest ROC-AUC of all ten studied models (0.977), which is an enhancement of 0.0787 in ROC-AUC against HRFLM.},
      keywords = {Heart disease prediction, Hybrid RF-ANN, HRFLM, Random Forest, ensemble learning, ROC-AUC.},
      month = {July},
      doi = {https://doi.org/10.64388/IREV10I1-1720179}
  }