Prediction of Heart Disease UCI Dataset Using Machine Learning Algorithms
DOI:
https://doi.org/10.21512/emacsjournal.v4i3.8683Keywords:
Machine Learning, Heart Disease, Classification, Feature Selection, PredictionAbstract
Heart disease is inflammation or damage to the heart and blood vessels over time. the disease can affect anyone of any age, gender, or social status. After many studies trying to overcome and learn about heart disease, in the end, this disease can be detected using machine learning systems. It predicts the likelihood of developing heart disease. The results of this system give the probability of heart disease as a percentage. Data collection using secret data mining. The data assets handled in python programming use two main algorithms for machine learning, the decision tree algorithm, and the Bayes naive algorithm which shows the best of both for heart disease accuracy. The results we get from this study show that the SVM algorithm is the algorithm with the most excellent precision. and the highest accuracy with a score of 85% in predicting heart disease using machine learning algorithms.Heart disease is inflammation or damage to the heart and blood vessels over time. the disease can affect anyone of any age, gender, or social status. After many studies trying to overcome and learn about heart disease, in the end, this disease can be detected using machine learning systems. It predicts the likelihood of developing heart disease. The results of this system give the probability of heart disease as a percentage. Data collection using secret data mining. The data assets handled in python programming use two main algorithms for machine learning, the decision tree algorithm, and the Bayes naive algorithm which shows the best of both for heart disease accuracy. The results we get from this study show that the SVM algorithm is the algorithm with the most excellent precision. and the highest accuracy with a score of 85% in predicting heart disease using machine learning algorithms.
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References
C. S. Dangare, S. S. Apte, and M. E. Student, “Improved Study of Heart Disease Prediction System using Data Mining Classification Techniques,” International Journal of Computer Applications, vol. 47, no. 10, pp. 975–888, 2012.
Chandra Reddy, N. S., Shue Nee, S., Zhi Min, L., & Xin Ying, C. (2019). Classification and Feature Selection Approaches by Machine Learning Techniques: Heart Disease Prediction. International Journal of Innovative Computing, 9(1), 39–46. https://doi.org/10.11113/ijic.v9n1.210
Chandra Reddy, N. S., Shue Nee, S., Zhi Min, L., & Xin Ying, C. (2019). Classification and Feature Selection Approaches by Machine Learning Techniques: Heart Disease Prediction. International Journal of Innovative Computing, 9(1), 39–46. https://doi.org/10.11113/ijic.v9n1.210
Nishadi, A. S. T. (n.d.). International Journal of Advanced Research and Publications Predicting Heart Diseases In Logistic Regression Of Machine Learning Algorithms By Python Jupyterlab. Retrieved March 22, 2022, from https://www.kaggle.com
Mukherji, D., Padalia, N., & Naidu, A. (2013). A Heart Disease Prediction Model using SVM-Decision Trees-Logistic Regression (SDL). International Journal of Computer Applications, 68(16), 975–8887.
Dikananda, A. R., Ali, I., Sai, V., Reddy, K., Meghana, P., Reddy, S., & Rao, A. (2022). Prediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers Genre e-sport gaming tournament classification using machine learning technique based on decision tree, Naïve Bayes, and random forest algorithm Prediction on Cardiovascular disease using Decision tree and Naïve Bayes classifiers. Journal of Physics: Conference Series, 2161, 12015. https://doi.org/10.1088/1742-6596/2161/1/012015
G, A., Ganesh, B., Ganesh, A., Srinivas, C., Dhanraj, & Mensinkal, K. (2022). Logistic regression technique for prediction of cardiovascular disease. Global Transitions Proceedings, 3(1), 127–130. https://doi.org/10.1016/J.GLTP.2022.04.008
Kumar, Rs., & Fatima, D. (n.d.). Heart Disease Prediction Using Extended KNN(E-KNN). International Journal of Advanced Trends in Computer Science and Engineering, 9(5), 8799–8803. https://doi.org/10.30534/ijatcse/2020/272952020
Rjeily, C. B., Badr, G., Hajjam, A., Andre`s, E., Hajjarm, A., Hassani, E., & Andres, E. (2019). Medical data mining for heart diseases and the future of sequential mining in medical field. Springer, 149, 71–99. https://doi.org/10.1007/978-3-319-94030-4 4
Ullah, F., Abdullah, A., Kaiwartya, O., systems, Y. C.-J. of medical, & 2017, undefined. (2017). TraPy-MAC: Traffic priority aware medium access control protocol for wireless body area network. Springer, 41(6), 93. https://doi.org/10.1007/s10916-017-0739-y
Ullah, F., Abdullah, A. H., Kaiwartya, O., & Arshad, M. M. (2017). Traffic Priority-Aware Adaptive Slot Allocation for Medium Access Control Protocol in Wireless Body Area Network. Computers 2017, Vol. 6, Page 9, 6(1), 9. https://doi.org/10.3390/COMPUTERS6010009
The relationship between recall and precision - buckland - 1994 ... (n.d.). Retrieved May 29, 2022, from https://asistdl.onlinelibrary.wiley.com/doi/abs/10.1002/%28SICI%291097- 4571%28199401%2945%3A1 %3C12%3A%3AAID-ASI2%3E3.0.CO%3B2-L
Jiao, Y., & Du, P. (2016, December 23). Performance measures in evaluating machine learning based bioinformatics predictors for classifications - quantitative biology. SpringerLink. Retrieved May 29, 2022, from https://link.springer.com/article/10.1007/s40484-016-0081-2
Xu, J., Zhang, Y., & Miao, D. (2019, July 11). Three-way confusion matrix for classification: A measure driven view. Information Sciences. Retrieved May 29, 2022, from https://www.sciencedirect.com/science/article/pii/S00200255193060
Rajdhan, A., Agarwal, A., & Sai, M. (n.d.). Heart Disease Prediction using Machine Learning. IJERT Journal International Journal of Engineering Research & Technology. Retrieved March 22, 2022, from www.ijert.org
Subhadra, K., Innovative, B. V.-I. J. of, & 2019, undefined. (n.d.). Neural network based intelligent system for predicting heart disease. Researchgate.Net, 2278–3075. Retrieved March 15, 2022, from https://www.researchgate.net/profile/Vikas-Boddu/publication/332035370_Neural_network_based_intelligent_system_for_predicting_heart_disease/links/601f7b36299bf1cc26ac05de/Neural-network-based-intelligent-system-for-predicting-heart-disease.
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