A comparison of predictions for cardiovascular disease using a variety of machine learning techniques
Keywords:
SVM, Random Forest, Decision tree, logistic regression, Machine learning, Cardiovascular disease predictionAbstract
One of the leading causes of global death is cardiovascular disease. They also pose a significant threat to people in general. Early access to heart disease detection is critical for increasing survival rates. There are multiple factors, such as age, gender, cholesterol, blood sugar, and heart rate, that can influence the risk of suffering from a life-threatening problem. However, it is difficult for experts to assess these variables when predicting a patient's risk profile. This study employs machine learning to estimate the likelihood of disease occurrence in patients. In this work, the authors propose applying those learning’s using the UCI Machine Learning Heart Diagnosis dataset and various machine learning algorithms. This study aims to compare, contrast, and evaluate the results of machine learning algorithms on the UCI Machine Learning Heart Disease dataset. The study also aims to determine which machine learning algorithms are well-suited for detecting heart disease. It is well known that computational methods can be a promising platform for disease prediction from the UCI Machine Learning Heart Diagnosis dataset. The commonly used machine learning algorithms include kernel configuration only (SVM), random forest, and decision tree, and additional machine learning algorithms. Each method offers substantial potential for disease prediction, making them helpful in treating cardiovascular diseases in general. We found that random forests are simple, and you can get good results with around 87.76% accuracy.
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W. Sun, P. Zhang, Z. Wang, and D. Li, "Prediction of cardiovascular diseases based on machine learning," ASP Transactions on Internet of Things, vol. 1, pp. 30-35, 2021.
[2] A. S. Alenizi and K. A. Al-karawi, "Machine learning approach for diabetes prediction," in International Congress on Information and Communication Technology, 2023, pp. 745-756.
[3] M. M. Abdelwahab, K. A. Al-Karawi, and H. Semary, "Integrating gene selection and deep learning for enhanced Autisms' disease prediction: a comparative study using microarray data," AIMS Mathematics, vol. 9, pp. 17827–17846, 2024.
[4] S. S. Bhuyan and A. K. Mishra, "Machine Learning Algorithms for Heart Disease Prediction," Asian Journal For Convergence In Technology (AJCT) ISSN-2350-1146, vol. 8, pp. 87-91, 2022.
[5] M. M. Abdelwahab, K. A. Al-Karawi, and H. E. Semary, "Deep learning-based prediction of Alzheimer’s disease using microarray gene expression data," Biomedicines, vol. 11, p. 3304, 2023.
[6] WHO. (2012). World health Organization, The top 10 causes of death. available at: . Available: http://www.who.int/mediacentre/factsheets/fs310/en/
[7] J. Pirgazi, A. Ghanbari Sorkhi, and M. Iranpour Mobarkeh, "An Accurate Heart Disease Prognosis Using Machine Intelligence and IoMT," Wireless Communications and Mobile Computing, vol. 2022, 2022.
[8] M. Sarveshvar, A. Gogoi, A. K. Chaubey, S. Rohit, and T. Mahesh, "Performance of different machine learning techniques for the prediction of heart diseases," in 2021 international conference on forensics, analytics, big data, security (FABS), 2021, pp. 1-4.
[9] H. Semary, K. A. Al-Karawi, and M. M. Abdelwahab, "Using Voice Technologies to Support Disabled People," Journal of Disability Research, vol. 3, p. 20230063, 2024.
[10] K. A. Al-Karawi, "Face mask effects on speaker verification performance in the presence of noise," Multimedia Tools and Applications, pp. 1-14, 2023.
[11] 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.
[12] A. S. Alenizi and K. A. Al-Karawi, "Internet of Things (IoT) Adoption: Challenges and Barriers," in Proceedings of Seventh International Congress on Information and Communication Technology, 2023, pp. 217-229.
[13] X. Yuan, J. Chen, K. Zhang, Y. Wu, and T. Yang, "A stable AI-based binary and multiple class heart disease prediction model for IoMT," IEEE Transactions on Industrial Informatics, vol. 18, pp. 2032-2040, 2021.
[14] E. Sogancioglu, K. Murphy, E. Calli, E. T. Scholten, S. Schalekamp, and B. Van Ginneken, "Cardiomegaly detection on chest radiographs: Segmentation versus classification," IEEE Access, vol. 8, pp. 94631-94642, 2020.
[15] F. Z. Abdeldjouad, M. Brahami, and N. Matta, "A hybrid approach for heart disease diagnosis and prediction using machine learning techniques," in The Impact of Digital Technologies on Public Health in Developed and Developing Countries: 18th International Conference, ICOST 2020, Hammamet, Tunisia, June 24–26, 2020, Proceedings 18, 2020, pp. 299-306.
[16] A. Tyagi and R. Mehra, "Intellectual heartbeats classification model for diagnosis of heart disease from ECG signal using hybrid convolutional neural network with GOA," SN Applied Sciences, vol. 3, pp. 1-14, 2021.
[17] A. Palmer, R. Jiménez, and E. Gervilla, "Data mining: Machine learning and statistical techniques," Knowledge-Oriented Applications in Data Mining, Prof. Kimito Funatsu (Ed.), pp. 373-396, 2011.
[18] K. A. Al-Karawi and D. Y. Mohammed, "Using combined features to improve speaker verification in the face of limited reverberant data," International Journal of Speech Technology, pp. 1-11, 2023.
[19] M.-L. Zhang and Z.-H. Zhou, "ML-KNN: A lazy learning approach to multi-label learning," Pattern recognition, vol. 40, pp. 2038-2048, 2007.
[20] N. Nadar and R. Kamatchi, "Information and communication-based collaborative learning and behavior modeling using machine learning algorithm," in Social Media and Machine Learning, ed: IntechOpen, 2019.
[21] S. Grampurohit and C. Sagarnal, "Disease prediction using machine learning algorithms," in 2020 International Conference for Emerging Technology (INCET), 2020, pp. 1-7.
[22] J.-M. Jo, "Effectiveness of normalization pre-processing of big data to the machine learning performance," The Journal of the Korea institute of electronic communication sciences, vol. 14, pp. 547-552, 2019.
[23] D. Paper, Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python: Apress, 2019.
[24] S. Kohara, T. Tateyama, A. H. Foruzan, A. Furukawa, S. Kanasaki, M. Wakamiya, et al., "Application of statistical shape model to diagnosis of liver disease," in The 2nd International Conference on Software Engineering and Data Mining, 2010, pp. 680-683.
[25] M. B. Butt, M. Alfayad, S. Saqib, M. Khan, M. Ahmad, M. A. Khan, et al., "Diagnosing the stage of hepatitis C using machine learning," Journal of Healthcare Engineering, vol. 2021, 2021.
[26] M. B. Butt, M. Alfayad, S. Saqib, M. Khan, M. Ahmad, M. A. Khan, et al., "Research Article Diagnosing the Stage of Hepatitis C Using Machine Learning," 2021.
[27] M. M. S. Raihan, A. B. Shams, and R. B. Preo, "Multi-class electrogastrogram (EGG) signal classification using machine learning algorithms," in 2020 23rd International conference on computer and information technology (ICCIT), 2020, pp. 1-6.
[28] J. Singh, S. Bagga, and R. Kaur, "Software-based prediction of liver disease with feature selection and classification techniques," Procedia Computer Science, vol. 167, pp. 1970-1980, 2020.
[29] S. Sontakke, J. Lohokare, and R. Dani, "Diagnosis of liver diseases using machine learning," in 2017 International Conference on Emerging Trends & Innovation in ICT (ICEI), 2017, pp. 129-133.
[30] S. Bahramirad, A. Mustapha, and M. Eshraghi, "Classification of liver disease diagnosis: A comparative study," in 2013 Second International Conference on Informatics & Applications (ICIA), 2013, pp. 42-46.
[31] L. A. Auxilia, "Accuracy prediction using machine learning techniques for indian patient liver disease," in 2018 2nd International Conference on Trends in Electronics and Informatics (ICOEI), 2018, pp. 45-50.
[32] A. S. Singh, M. Irfan, and A. Chowdhury, "Prediction of liver disease using classification algorithms," in 2018 4th international conference on computing communication and automation (ICCCA), 2018, pp. 1-3.
[33] N. Nahar, F. Ara, M. A. I. Neloy, V. Barua, M. S. Hossain, and K. Andersson, "A comparative analysis of the ensemble method for liver disease prediction," in 2019 2nd International Conference on Innovation in Engineering and Technology (ICIET), 2019, pp. 1-6.
