Heart Disease Diagnosis via Web Based Classification Software programmed with Julia Programming Language

Year 2024, Volume: 4 Issue: 1, 1 - 10, 01.05.2024

Emek Güldoğan , Hüseyin Kutlu , Cemil Çolak

Abstract

In recent years, various tools and algorithms have been proposed and continue to be proposed by researchers to develop highly successful medical decision support systems. However, the clinical use of these algorithms is very limited due to various limitations. Making the necessary software installations to run the algorithm, lack of programming knowledge are some of these restrictions. In this study, a web-based classification software developed with the Julia programming language, which can be used by physicians in their medical research and clinical decisions, is introduced. Through this software, coronary artery disease detection was performed with the Cleveland heart disease database, which is a publicly accessible data set. The dataset was classified with eight different classifiers (KNN, SVM, DT, RF, AdaBoost, Gauss Naive Bayes, LDA, LR) supported by the software. The metrics obtained by 10-fold cross-validation of the data set are reported. The SVM classifier achieved the highest classification accuracy with 86.44%. The software proposed in this study may assist clinicians in research and patient identification.

Keywords

julia, Dash, Classification, Heart Disease, Web-based Artificial Intelligence Software

References

• [1] R. T. Sutton, D. Pincock, D. C. Baumgart, D. C. Sadowski, R. N. Fedorak, and K. I. Kroeker, “An overview of clinical decision support systems: benefits, risks, and strategies for success,” NPJ Digit Med, vol. 3, no. 1, p. 17, Feb. 2020, doi: 10.1038/s41746-020-0221-y.
• [2] I. Sim et al., “Clinical Decision Support Systems for the Practice of Evidence-based Medicine,” Journal of the American Medical Informatics Association, vol. 8, no. 6, pp. 527–534, Nov. 2001, doi: 10.1136/jamia.2001.0080527.
• [3] J. Amann et al., “To explain or not to explain?—Artificial intelligence explainability in clinical decision support systems,” PLOS Digital Health, vol. 1, no. 2, p. e0000016, Feb. 2022, doi: 10.1371/journal.pdig.0000016.
• [4] K. Gao, G. Mei, F. Piccialli, S. Cuomo, J. Tu, and Z. Huo, “Julia language in machine learning: Algorithms, applications, and open issues,” Comput Sci Rev, vol. 37, p. 100254, Aug. 2020, doi: 10.1016/j.cosrev.2020.100254.
• [5] D. W. Aha, “ Heart Disease Data Set,” https://archive.ics.uci.edu/ml/datasets/heart+disease, Jan. 26, 2023.
• [6] R. Das, I. Turkoglu, and A. Sengur, “Effective diagnosis of heart disease through neural networks ensembles,” Expert Syst Appl, vol. 36, no. 4, pp. 7675–7680, May 2009, doi: 10.1016/j.eswa.2008.09.013.
• [7] R. Das, I. Turkoglu, and A. Sengur, “Effective diagnosis of heart disease through neural networks ensembles,” Expert Syst Appl, vol. 36, no. 4, pp. 7675–7680, May 2009, doi: 10.1016/j.eswa.2008.09.013.
• [8] N. Ghadiri Hedeshi and M. Saniee Abadeh, “Coronary Artery Disease Detection Using a Fuzzy-Boosting PSO Approach,” Comput Intell Neurosci, vol. 2014, pp. 1–12, 2014, doi: 10.1155/2014/783734.
• [9] Heart tests, “https://www.heartfoundation.org.nz/your-heart/heart-tests,” https://www.heartfoundation.org.nz/your-heart/heart-tests, Jan. 25, 2023.
• [10] A. Javeed, S. Zhou, L. Yongjian, I. Qasim, A. Noor, and R. Nour, “An Intelligent Learning System Based on Random Search Algorithm and Optimized Random Forest Model for Improved Heart Disease Detection,” IEEE Access, vol. 7, pp. 180235–180243, 2019, doi: 10.1109/ACCESS.2019.2952107.
• [11] S. J. Pasha and E. S. Mohamed, “Novel Feature Reduction (NFR) Model With Machine Learning and Data Mining Algorithms for Effective Disease Risk Prediction,” IEEE Access, vol. 8, pp. 184087–184108, 2020, doi: 10.1109/ACCESS.2020.3028714.
• [12] S. M. Saqlain et al., “Fisher score and Matthews correlation coefficient-based feature subset selection for heart disease diagnosis using support vector machines,” Knowl Inf Syst, vol. 58, no. 1, pp. 139–167, Jan. 2019, doi: 10.1007/s10115-018-1185-y.
• [13] Y. Muhammad, M. Tahir, M. Hayat, and K. T. Chong, “Early and accurate detection and diagnosis of heart disease using intelligent computational model,” Sci Rep, vol. 10, no. 1, p. 19747, Nov. 2020, doi: 10.1038/s41598-020-76635-9.
• [14] L. Ali, A. Rahman, A. Khan, M. Zhou, A. Javeed, and J. A. Khan, “An Automated Diagnostic System for Heart Disease Prediction Based on ${\chi^{2}}$ Statistical Model and Optimally Configured Deep Neural Network,” IEEE Access, vol. 7, pp. 34938–34945, 2019, doi: 10.1109/ACCESS.2019.2904800.
• [15] C. Gupta, A. Saha, N. v Subba Reddy, and U. Dinesh Acharya, “Cardiac Disease Prediction using Supervised Machine Learning Techniques.,” J Phys Conf Ser, vol. 2161, no. 1, p. 012013, Jan. 2022, doi: 10.1088/1742-6596/2161/1/012013.
• [16] Andras Janosi, William Steinbrunn, Matthias Pfisterer, and Robert Detrano, “ Heart Disease Data Set,” https://archive.ics.uci.edu/ml/datasets/heart+disease, Jan. 25, 2023.
• [17] “Veri Bilimi ve Yapay Zekâ Tabanlı Web Yazılımları ,” http://biostatapps.inonu.edu.tr/.
• [18] “Veri Sınıflandırma Yazılımı,” http://biostatapps.inonu.edu.tr/JVSY/.
• [19] G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, “KNN Model-Based Approach in Classification,” 2003, pp. 986–996. doi: 10.1007/978-3-540-39964-3_62.
• [20] Y. I. A. Rejani and S. T. Selvi, “Early Detection of Breast Cancer using SVM Classifier Technique,” Dec. 2009.
• [21] C. Kingsford and S. L. Salzberg, “What are decision trees?,” Nat Biotechnol, vol. 26, no. 9, pp. 1011–1013, Sep. 2008, doi: 10.1038/nbt0908-1011.
• [22] M. Pal, “Random forest classifier for remote sensing classification,” Int J Remote Sens, vol. 26, no. 1, pp. 217–222, Jan. 2005, doi: 10.1080/01431160412331269698.
• [23] R. E. Schapire, “Explaining AdaBoost,” in Empirical Inference, Berlin, Heidelberg: Springer Berlin Heidelberg, 2013, pp. 37–52. doi: 10.1007/978-3-642-41136-6_5.
• [24] H. Kamel, D. Abdulah, and J. M. Al-Tuwaijari, “Cancer Classification Using Gaussian Naive Bayes Algorithm,” in 2019 International Engineering Conference (IEC), Jun. 2019, pp. 165–170. doi: 10.1109/IEC47844.2019.8950650.
• [25] Haoshi Zhang, Yaonan Zhao, Fuan Yao, Lisheng Xu, Peng Shang, and Guanglin Li, “An adaptation strategy of using LDA classifier for EMG pattern recognition,” in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jul. 2013, pp. 4267–4270. doi: 10.1109/EMBC.2013.6610488.