Heart Disease Prediction and GUI Interaction based on Machine Learning

Authors

  • Yimin Yang

DOI:

https://doi.org/10.62051/z195z943

Keywords:

Heart Disease Prediction; Machine Learning; Recall Rates; Support Vector Machines; Graphical User Interface.

Abstract

Machine learning is now being used to detect heart disease. Considering that failure to diagnose a heart disease patient can lead to serious consequences, including delayed treatment, worsening of the condition, and even life-threatening situations, it is crucial to ensure that as many true patients as possible are confirmed. Thus, it is important to consider recall rates while maintaining a focus on accuracy. This article compares the application of four machine learning models, including Decision Trees (DT), Random Forests (RF), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM), in heart disease prediction, and measures the effectiveness of these models by using accuracy, recall rates, and F1 score. The outcomes of the experiment reveal that the SVM model performs the best with a recall rate of 0.97. The balance of the model ensures that it achieves high recall without affecting accuracy. In addition, the author combines it with a Graphical User Interface (GUI) to achieve interactive effects. The model and its interactive functions selected in this experiment can easily avoid missing patients in the first screening and improve the accuracy of disease diagnosis.

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References

Kanwal, Amna, K. Tehseen Ahmad, and N. Aslam. Detection of Heart Disease Using Supervised Machine Learning. 2022.

Ali, M. Mamun, et al. Heart disease prediction using supervised machine learning algorithms: Performance analysis and comparison. Computers in Biology and Medicine 136, 2021, p. 104672.

M. Aljanabi, H. Mahmoud. Qutqut, and M. Hijjawi. Machine learning classification techniques for heart disease prediction: a review. International Journal of Engineering & Technology 7(4), 2018, pp. 5373-5379.

M. Marimuthu, et al. A review on heart disease prediction using machine learning and data analytics approach. International Journal of Computer Applications, 181(18), 2018, pp. 20-25.

Beyene, Chala, and P. Kamat. Survey on prediction and analysis the occurrence of heart disease using data mining techniques. International Journal of Pure and Applied Mathematics, 118(8), 2018, pp. 165-174.

K. Polaraju and D. Durga Prasad. Prediction of heart disease using multiple linear regression model. International Journal of Engineering Development and Research Development, 5(4), 2017, pp. 1419-1425.

S. Prabhavathi and D.M. Chitra. Analysis and prediction of various heart diseases using DNFS techniques. International journal of innovations in scientific and engineering research, 2(1), 2016, pp. 1-7.

Deepika, Kumari, and S. Seema. Predictive analytics to prevent and control chronic diseases. international conference on applied and theoretical computing and communication technology (iCATccT). IEEE, 2016.

M. Jabbar, Akhil, B.L. Deekshatulu, and P. Chandra. Heart disease classification using nearest neighbor classifier with feature subset selection. Seria Informatica 11, 2013, pp. 47-54.

Pal, Madhumita, and S. Parija. Prediction of heart diseases using random forest. Journal of Physics: Conference Series. 1817(1), 2021.

Information on: https://www.kaggle.com/datasets/arezaei81/heartcsv.

Shalev-Shwartz, Shai, and S. Ben-David. Understanding machine learning: From theory to algorithms. Cambridge university press, 2014.

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Published

12-08-2024

How to Cite

Yang, Y. (2024) “Heart Disease Prediction and GUI Interaction based on Machine Learning ”, Transactions on Computer Science and Intelligent Systems Research, 5, pp. 209–218. doi:10.62051/z195z943.