Abstract
Heart attack or heart failure cases are rising quickly each day, thus it is crucial and worrisome to anticipate any problems in advance. A heart attack is a significant medical emergency that happens when the blood circulation to the heart is abruptly clogged, normally by a blood clot. For the prevention and treatment of heart failure, an accurate and prompt identification of heart disease is essential. Traditional medical history has been criticized for not being a trustworthy method of diagnosing heart disease in many ways. Machine learning techniques are effective and reliable for classifying healthy individuals from heart attack risk factors. This study proposes a model based on machine learning methods such as decision trees, random forests, neural networks, voting, gradient boosting, and logistic regression using a dataset from the UCI repository that incorporates numerous heart disease-related variables. The aim of this paper is to foresee the probability of a heart attack or failure in patients. According to the results, the gradient boosting approach exhibits the best performance in terms of accuracy, precision, recall, specificity, and f1-score. Decision tree, random forest, voting, and gaussian naive Bayes also have shown good performance.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ahmed, Hager, et al. (2020) Heart disease identification from patients’ social posts, machine learning solution on Spark. Futur Gener Comput Syst 111: 714–722.
Mohd Amiruddin, Ahmad Azharuddin Azhari, et al. (2020) Neural network applications in fault diagnosis and detection: an overview of implementations in engineering-related systems. Neural Comput Appl 32.2 447–472
Al Badarin, Firas J, Saurabh Malhotra. (2019) Diagnosis and prognosis of coronary artery disease with SPECT and PETCurr Cardiol Rep 21.7: 1–11
Budholiya, Kartik, Shailendra Kumar Shrivastava, Vivek Sharma (2020) An optimized XGBoost based diagnostic system for effective prediction of heart disease. J King Saud Univ-Comput Inf Sci
Bui AL, Horwich TB, Fonarow GC (2011) Epidemiology and risk profile of heart failure. Nat Rev Cardiol 8(1):30–41
David H, Antony Belcy S (2018) Heart disease prediction using data mining techniques. ICTACT J. Soft Comput 9.1
Desai, Shrinivas D et al. (2019) Back-propagation neural network versus logistic regression in heart disease classification. Adv Comput Commun Technol. Springer, Singapore. 133–144
Dibben Grace et al. (2021) Exercise‐based cardiac rehabilitation for coronary heart disease. Cochrane Database Syst Rev 11
Faruqui, Nuruzzaman, et al. (2021) LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data. Comput Biol Med 139: 104961
Haq, Amin Ul et al. (2019) Heart disease prediction system using model of machine learning and sequential backward selection algorithm for features selection.In: 2019 IEEE 5th International Conference for Convergence in Technology (I2CT). IEEE
Heart disease and stroke statistics update fact sheet at-a-glance. (n.d.), https://professional.heart.org/en/science-news/-/media/8D840F1AA88D423888ED3BA96DD61010.ashx , last accessed 2022/08/05
NHS Homepage, https://www.nhs.uk/conditions/heart-attack/, last accessed 2022/07/22
Hossen, Rakib et al. (2021) BDPS: An efficient spark-based big data processing scheme for cloud Fog-IoT Orchestration. Information 12.12: 517
Ishaq, Abid, et al. (2021) Improving the prediction of heart failure patients’ survival using SMOTE and effective data mining techniques. IEEE access 9: 39707–39716
Jindal, Harshit, et al. (2021) Heart disease prediction using machine learning algorithms.In: IOP conference series: materials science and engineering. 1022(1). IOP Publishing
Kaggle, https://www.kaggle.com/datasets/rashikrahmanpritom/heart-attack-analysis-prediction-dataset, last accessed 2022/07/22
Khourdifi Y, Bahaj M (2019) Heart disease prediction and classification using machine learning algorithms optimized by particle swarm optimization and ant colony optimization. Int J Intell Eng Syst 12(1):242–252
Van Klompenburg, Thomas, Ayalew Kassahun, Cagatay Catal (2020) Crop yield prediction using machine learning: A systematic literature review. Comput Electron Agric 177: 105709
Minou, John, et al. (2020) Classification techniques for cardio-vascular diseases using supervised machine learning.“ Medical Archives 74.1: 39
Phasinam, Khongdet, et al. (2022) Analyzing the performance of machine learning techniques in disease prediction. J Food Qual 2022
Plati, Dafni K, et al. (2021) A machine learning approach for chronic heart failure diagnosis. Diagnostics 11.10: 1863
Rani, Pooja, Rajneesh Kumar, Anurag Jain (2021) Multistage model for accurate prediction of missing values using imputation methods in heart disease dataset. Innov Data Commun Technol Appl. Springer, Singapore. 637–653
Sagar, Shuvashish Paul, et al. (2021) PRCMLA: Product review classification using machine learning algorithms. In: Proceedings of International Conference on Trends in Computational and Cognitive Engineering. Springer, Singapore
Shah, Devansh, Samir Patel, Santosh Kumar Bharti (2020) Heart disease prediction using machine learning techniques. SN Comput Sci 1.6: 1–6
Ullah, Farhat, et al. (2022) An efficient machine learning model based on improved features selections for early and accurate heart disease predication. Comput Intell Neurosci 2022
Whaiduzzaman, Md, et al. (2020) AUASF: An anonymous users authentication scheme for fog-IoT environment. In: 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE
Whaiduzzaman, Md, et al. (2021) HIBAF: A data security scheme for fog computing. J High Speed Netw Preprint: 1–22
Zhang Y, Haghani A (2015) A gradient boosting method to improve travel time prediction. Transp Res Part C: Emerg Technol 58:308–324
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Oliullah, K., Barros, A., Whaiduzzaman, M. (2023). Analyzing the Effectiveness of Several Machine Learning Methods for Heart Attack Prediction. In: Kaiser, M.S., Waheed, S., Bandyopadhyay, A., Mahmud, M., Ray, K. (eds) Proceedings of the Fourth International Conference on Trends in Computational and Cognitive Engineering. Lecture Notes in Networks and Systems, vol 618. Springer, Singapore. https://doi.org/10.1007/978-981-19-9483-8_19
Download citation
DOI: https://doi.org/10.1007/978-981-19-9483-8_19
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-9482-1
Online ISBN: 978-981-19-9483-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)