The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Heart Disease Prediction using Hybrid machine Learning Model
Abstract
Heart disease causes a significant mortality rate around the world, and it has become a health threat for many people. Early prediction of heart disease may save many lives; detecting cardiovascular diseases like heart attacks, coronary artery diseases etc., is a critical challenge by the regular clinical data analysis. Machine learning (ML) can bring an effective solution for decision making and accurate predictions. The medical industry is showing enormous development in using machine learning techniques. In the proposed work, a novel machine learning approach is proposed to predict heart disease. The proposed study used the Cleveland heart disease dataset, and data mining techniques such as regression and classification are used. Machine learning techniques Random Forest and Decision Tree are applied. The novel technique of the machine learning model is designed. In implementation, 3 machine learning algorithms are used, they are 1. Random Forest, 2. Decision Tree and 3. Hybrid model (Hybrid of random forest and decision tree). Experimental results show an accuracy level of 88.7% through the heart disease prediction model with the hybrid model. The interface is designed to get the user's input parameter to predict the heart disease, for which we used a hybrid model of Decision Tree and Random Forest.
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Citations
Cites
Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques
Mohan, S., Thirumalai, C., & Srivastava, G. (2019) · IEEE Access · doi:10.1109/access.2019.2923707 · Cited by 1,951 · cited 1×
“[5] created an efficient hybrid random forest with a linear model (HRFLM) to improve the accuracy of heart disease prediction.”
MethodIntelligent heart disease prediction system using data mining techniques
Palaniappan, S., & Awang, R. (2008) · 2008 IEEE/ACS International Conference on Computer Systems and Applications · doi:10.1109/aiccsa.2008.4493524 · Cited by 816
Predictive Data Mining for Medical Diagnosis: An Overview of Heart Disease Prediction
Soni, J., Ansari, U., Sharma, D., & Soni, S. (2011) · International Journal of Computer Applications · doi:10.5120/2237-2860 · Cited by 706
Effective diagnosis of heart disease through neural networks ensembles
Das, R., Turkoglu, I., & Sengur, A. (2009) · Expert Systems with Applications · doi:10.1016/j.eswa.2008.09.013 · Cited by 628
Heart Disease Prediction Using Machine Learning Algorithms
Singh, A., & Kumar, R. (2020) · 2020 International Conference on Electrical and Electronics Engineering (ICE3) · doi:10.1109/ice348803.2020.9122958 · Cited by 381
Heart disease prediction using machine learning techniques : a survey
V. Ramalingam, V, Dandapath, A., & Karthik Raja, M. (2018) · International Journal of Engineering & Technology · doi:10.14419/ijet.v7i2.8.10557 · Cited by 368
Using data mining techniques in heart disease diagnosis and treatment
Shouman, M., Turner, T., & Stocker, R. (2012) · Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC) · doi:10.1109/jec-ecc.2012.6186978 · Cited by 154
Polat, K., Şahan, S., & Güneş, S. (2006) · Expert Systems with Applications · doi:10.1016/j.eswa.2006.01.027 · Cited by 136
A Heart Disease Prediction Model using SVM-Decision Trees-Logistic Regression (SDL)
Mythili, T., Mukherji, D., Padalia, N., & Naidu, A. N. B. (2013) · International Journal of Computer Applications · doi:10.5120/11662-7250 · Cited by 129
Using PSO Algorithm for Producing Best Rules in Diagnosis of Heart Disease
Alkeshuosh, A. H., Moghadam, M. Z., Mansoori, I. A., & Abdar, M. (2017) · International Conference on Computer and Applications (ICCA) · doi:10.1109/comapp.2017.8079784 · Cited by 107
Cited by
Michel, J., Manns, A., Boudersa, S., Jaubert, C., Dupic, L., Vivien, B., Burgun, A., Campeotto, F., & Tsopra, R. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105347 · Cited by 26
Metadata
- Authors
- Kavitha, M., Gnaneswar, G., Dinesh, R., Sai, Y. R., & Suraj, R. S.
- Venue
- 2021 6th International Conference on Inventive Computation Technologies (ICICT)
- Publisher
- IEEE
- Published
- 2021-01-20
- Pages
- 1329-1333
- OpenAlex
- W3134840015
- Cited by
- 429
- References
- 12
- FWCI
- 59.33
- Type
- conference paper
- Language
- en
- Open access
- closed
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