• P-ISSN 0974-6846 E-ISSN 0974-5645

Indian Journal of Science and Technology


Indian Journal of Science and Technology

Year: 2023, Volume: 16, Issue: 41, Pages: 3679-3690

Original Article

An Advanced Model to Predict Heart Disease Applying Random Forest Classifier and Whale Optimization Algorithm

Received Date:13 July 2023, Accepted Date:11 September 2023, Published Date:11 November 2023


Background: In this article a model for heart disease prediction has been proposed using machine learning to address the significant concern of lives lost due to this disease, especially in remote and underprivileged areas where access to proper medical support is lacking. Identifying the disease at an early stage is crucial for preventing unnecessary fatalities. Methods: The dataset has been collected from UCI data repository. The raw data set consists of 14 features, which are age, sex, cp, tresbps, chol, fbs, restecg, thalach, exang, oldpeak, slope, thal, ca, target. The collected dataset has been standardized applying StandardScaler and then feature selection has been carried out applying SelectKBest to select 9 most competent features. The selected features are age, sex, cp, thalach, exang, oldpeak, slope, ca, thal and target. 80% of the dataset has been used for training and remaining for testing. For classification Random Forest has been applied and the model performance has been increased applying Whale Optimization Algorithm. Findings: The WOA, inspired by the social behavior of humpback whales, has been utilized to optimize the parameters and configurations of the model. The proposed model has achieved 86.53% accuracy. Novelty: The proposed model combines the Random Forest classifier, with the SelectKBest to select optimal number of features through experiments which ensures that the model is trained only with effective features. This dimensionality reduction makes the model computationally efficient. More over application of Whale Optimization has increased the performance of model with 0.93 AUC score. This model’s novelty lies in its targeted focus on undeserved populations, aiming to improve early identification of heart disease and reduce fatalities in resource constrained areas.

Keywords: Heart Disease Prediction, Random Forest Classifier, Standardscaler, Whale Optimization, SelectKBest


  1. Sharma V, Yadav S, Gupta M. Heart Disease Prediction using Machine Learning Techniques. 2nd International Conference on Advances in Computing, Communication Control and Networking (ICACCCN). 2020;p. 177–181. Available from: https://doi.org/10.1109/ICACCCN51052.2020.9362842
  2. Yazdani A, Varathan KD, Chiam YK, Malik AW, Ahmad WAW. A novel approach for heart disease prediction using strength scores with significant predictors. BMC Medical Informatics and Decision Making. 2021;21(1):194. Available from: https://doi.org/10.1186/s12911-021-01527-5
  3. Bharti R, Khampari A, Shabaz M, Dhiman G, Pande S, Singh P. Prediction of Heart Disease Using a Combination of Machine Learning and Deep Learning. Computational Intelligence and Neuroscience. 2021;p. 11. Available from: https://doi.org/10.1155/2021/8387680
  4. Chang V, Vallabhanentrupabhavani A, Qianwenxu, Hossain M. An artificial intelligence model for heart disease detection using machine learning algorithms. Healthcare Analytics. 2022;2. Available from: https://doi.org/10.1016/j.health.2022.100016
  5. Reddy KVV, Elamvazuthi I, Aziz AA, Paramasivam S, Chua HN, Pranavanand S. Heart Disease Risk Prediction Using Machine Learning Classifiers with Attribute Evaluators. Applied Sciences. 2021;11(18):8352. Available from: https://doi.org/10.3390/app11188352
  6. Karpagam S, Kaleeswari M, Kavitha K, Priyadarsini S. Heart Disease Prediction Using Machine learning Algorithm. International Journal of Scientific Development and Research. 2020;5(8). Available from: https://www.ijsdr.org/papers/IJSDR2008043.pdf
  7. Nanthini K, Preethi S, Venkateshwaran S. Heart Disease Prediction Using Machine Learning Algorithms. International Journal of Advanced Science and Technology. 2020;29(3):9965. Available from: http://sersc.org/journals/index.php/IJAST/article/view/26971
  8. Muktevisrivenkatesh. Prediction of Cardiovascular Disease using Machine Learning Algorithms. International Journal of Engineering and Advanced Technology (IJEAT). 2020;(9) 2249–8958. Available from: https://www.ijeat.org/wp-content/uploads/papers/v9i3/B3986129219.pdf
  9. Senthilkumar M, Segar TC, Srivastava G. Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques. Institute of Electrical and Electronics Engineers . (7) 81542–81554. Available from: https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8740989&tag=1
  10. Saleh F, A. Implementation of Machine Learning Model to Predict Heart Failure Disease. International Journal of Advanced Computer Science and Applications(IJACSA). 2019;10(6). Available from: https://thesai.org/Publications/ViewPaper?Volume=10&Issue=6&Code=IJACSA&SerialNo=37
  11. Bhatt CM, Patel P, Ghetia T, Mazzeo PL. Effective Heart Disease Prediction Using Machine Learning Techniques. Algorithms. 2023;16(2):88. Available from: https://doi.org/10.3390/a16020088
  12. Biswas N, Ali MM, Rahaman MA, Islam M, Mia MR, Azam S, et al. Precision Medicine and Big Data Research Progress in Inflammatory Diseases. 2023. Available from: https://doi.org/10.1155/2023/6864343
  13. Ghazal TM, Ibrahim A, Akram AS, Qaisar ZH, Munir S, Islam S. Heart Disease Prediction Using Machine Learning. In: 2023 International Conference on Business Analytics for Technology and Security (ICBATS). (pp. 1-6) IEEE. 2023.
  14. Subramani S, Varshney N, Anand MV, Soudagar MEM, Al-Keridis LA, Upadhyay TK, et al. Cardiovascular diseases prediction by machine learning incorporation with deep learning. Frontiers in Medicine. 2023;10:1150933. Available from: https://doi.org/10.3389/fmed.2023.1150933
  15. Pal M, Parija S. Prediction of Heart Diseases using Random Forest.1 and Smita Parija.v2021. Journal of Physics: Conference Series. 2009. Available from: https://doi.org/10.1088/1742-6596/1817/1/012009
  16. Lutimath NM, Sharma N, Byregowda BK. Prediction of Heart Disease using Random Forest. In: 2021 Emerging Trends in Industry 4.0 . (pp. 1-4) IEEE. 2021.
  17. Dhaka P, Nagpal B. WoM-based deep BiLSTM: smart disease prediction model using WoM-based deep BiLSTM classifier. Multimedia Tools and Applications. 2023;82(16):25061–25082. Available from: https://doi.org/10.1007/s11042-023-14336-x
  18. Wei X, Rao C, Xiao X, Chen L, Goh M. Risk assessment of cardiovascular disease based on SOLSSA-CatBoost model. Expert Systems with Applications. 2023;219:119648. Available from: https://doi.org/10.1016/j.eswa.2023.119648
  19. Asadi S, Roshan S, Kattan MW. Random forest swarm optimization-based for heart diseases diagnosis. Journal of Biomedical Informatics. 2021;115:103690. Available from: https://doi.org/10.1016/j.jbi.2021.103690


© 2023 Majumder et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)


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