Abstract
Cardiovascular diseases (CVD) are a major cause of mortality world-wide causing about 17.9 million deaths per year. Cardiovascular illnesses are a group of conditions that affect the heart and blood arteries. These illnesses may have an effect on various parts of the heart and/or blood vessels. CVD encompasses coronary artery disorders (CAD), such as myocardial infarction and angina. To reduce the risk and deaths caused by cardiovascular diseases it is important to predict it at an early stage. It is crucial to be aware of these cardiac disease-related signs in order to forecast outcomes and offer a solid foundation for diagnosis for which data mining and feature selection prove to be useful. However, manual analysis and prediction are laborious and tiring due to the sheer volume of data. In this study, data science is used to predict cardiac problems. The potential method for heart disease prediction is one that analyses the relationships between variables and extracts hidden knowledge from the data. Through a variety of indications, our study attempts to anticipate cardiac disease correctly and promptly. We propose a cardiovascular disease prediction model which uses a dataset obtained from Kaggle on which we perform various data pre-processing techniques on which feature selection is done and the refined data is given to different machine learning models for the prediction of the disease. We obtained the highest accuracy of 99.4% using Random Forest, demonstrating the effectiveness and dependability of the heart disease prediction approach we presented.
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References
Zhang, D., et al.: Heart disease prediction based on the embedded feature selection method and deep neural network. J. Healthc. Eng. 2021, 1–9 (2021)
Bashir, S., et al.: Improving heart disease prediction using feature selection approaches. In: 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST). IEEE (2019)
Le, H.M., Tran, T.D., Van Tran, L.A.N.G.: Automatic heart disease prediction using feature selection and data mining technique. J. Comput. Sci. Cybernet. 34(1), 33–48 (2018)
Anuradha, P., David, V.K.: Feature selection and prediction of heart diseases using gradient boosting algorithms. In: 2021 International Conference on Artificial Intelligence and Smart Systems (ICAIS). IEEE (2021)
Mohan, S., Thirumalai, C., Srivastava, G.: Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7, 81542–81554 (2019)
Riyaz, L., et al.: Heart disease prediction using machine learning techniques: a quantitative review. In: International Conference on Innovative Computing and Communications. Springer, Singapore (2022). https://doi.org/10.1007/978-981-16-3071-2_8
Boukhatem, C., Youssef, H.Y., Nassif, A.B.: Heart disease prediction using machine learning. In: 2022 Advances in Science and Engineering Technology International Conferences (ASET). IEEE (2022).
Ayon, S.I., Islam, M.M., Hossain, M.R.: Coronary artery heart disease prediction: a comparative study of computational intelligence techniques. IETE J. Res. 68(4), 2488–2507 (2022)
Wang, M., et al.: Artificial intelligence models for predicting cardiovascular diseases in people with type 2 diabetes: a systematic review. Intell. Based Med. 6, 100072 (2022)
Ahsan, M.M., Siddique, Z.: Machine learning-based heart disease diagnosis: A systematic literature review. Artif. Intell. Med. 128, 102289 (2022)
Kukar, M., et al.: Analysing and improving the diagnosis of ischaemic heart disease with machine learning. Artif. Intell. Med. 16(1), 25–50 (1999)
Saikumar, K., Rajesh, V., Babu, B.S.: Heart disease detection based on feature fusion technique with augmented classification using deep learning technology. Traitement du Signal 39, 1 (2022)
Dunbray, N., et al.: An analytical survey on heart attack prediction techniques based on machine learning and IoT. In: Proceeding of International Conference on Computational Science and Applications, pp. 299–312. Springer, Singapore (2022). https://doi.org/10.1007/978-981-19-0863-7_24
Πεταρούδας, Μιλτιάδης Γεωργίου. Comparative analysis of machine learning techniques in predicting heart attacks. Diss. Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης ( 2022)
El-Hasnony, I.M., et al.: Multi-label active learning-based machine learning model for heart disease prediction. Sensors 22(3), 1184 (2022)
Aggarwal, R., Kumar, S.: An automated perception and prediction of heart disease based on machine learning. AIP Conf. Proc. 2424, 1 (2022). AIP Publishing LLC
Lakshmanaprabu, S.K., et al.: Optimal deep learning model for classification of lung cancer on CT images. Futur. Gener. Comput. Syst. 92, 374–382 (2019)
Derbali, M., et al.: Water desalination fault detection using machine learning approaches: a comparative study. IEEE Access 5, 23266–23275 (2017)
Al-Darraji, I., et al.: Adaptive robust controller design-based RBF neural network for aerial robot arm model. Electronics 10(7), 831 (2021)
Mohanty, S.N., et al.: Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks. Phys. Commun. 40, 101097 (2020)
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Vanami, N.V.J., Chintalapati, L.R., Challagundla, Y., Mohanty, S.N. (2024). Feature Selection Using Data Mining Techniques for Prognostication of Cardiovascular Diseases. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-031-50571-3_24
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DOI: https://doi.org/10.1007/978-3-031-50571-3_24
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