Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases


Year : 2024 | Volume : 11 | Issue : 02 | Page : 36-42
    By

    Sibi Sebastian,

  • Indra Vijay Singh,

  • Abdul Kareem,

  1. Student, Department of Artificial Intelligence and Machine Learning, Moodlakatte Institute of Technology, Kundapur, Karnataka, India
  2. Associate Professor, Department of Artificial Intelligence and Machine Learning, Moodlakatte Institute of Technology, Kundapur, Karnataka, India
  3. Professor, Department of Artificial Intelligence and Machine Learning, Moodlakatte Institute of Technology, Kundapur, Karnataka, India

Abstract

Cardiovascular diseases (CVDs) remain a major global health issue, highlighting the need for improved early detection and risk assessment methods. This research investigates the efficacy of both deep learning and traditional machine learning methods in forecasting cardiovascular diseases (CVDs). We evaluate a variety of models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Multilayer Perceptrons (MLPs), Long Short-Term Memory (LSTM) networks, as well as Logistic Regression (LR), Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), Gaussian Naive Bayes (NB), and K-Nearest Neighbors (KNN). Our analysis shows that the RNN model delivers the highest accuracy, achieving 99.51%, outperforming all other models. CNNs and Random Forests also show strong performance, while the LSTM model is less effective in comparison. These findings underscore the superior performance of deep learning techniques, especially RNNs, in improving CVD prediction accuracy. Integrating deep learning with traditional machine learning techniques enhances predictive accuracy, which is essential for advancing healthcare. By applying CNNs for extracting features from medical images and RNNs for analyzing patient data sequences, healthcare providers can achieve more accurate early detection and better management of CVDs. Furthermore, the effectiveness of Random Forests highlights the benefits of ensemble methods in handling complex medical data. This study contributes to the increasing body of evidence that supports the integration of advanced deep learning techniques with traditional machine learning algorithms for medical diagnostics. Such advancements have the potential to revolutionize cardiovascular care by enabling timely interventions and personalized treatment plans, thereby reducing the global burden of CVD-related deaths and illnesses.

Keywords: Deep learning, cardiovascular, machine learning, algorithms, healthcare

[This article belongs to Recent Trends in Parallel Computing (rtpc)]

How to cite this article:
Sibi Sebastian, Indra Vijay Singh, Abdul Kareem. Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases. Recent Trends in Parallel Computing. 2024; 11(02):36-42.
How to cite this URL:
Sibi Sebastian, Indra Vijay Singh, Abdul Kareem. Cutting-edge Deep Learning Methods for Predicting and Detecting Cardiovascular Diseases. Recent Trends in Parallel Computing. 2024; 11(02):36-42. Available from: https://journals.stmjournals.com/rtpc/article=2024/view=157390


References

  1. Seckeler MD, Hoke TR. The worldwide epidemiology of acute rheumatic fever and rheumatic heart disease. Clin Epidemiol. 2011; 3: 67–84.
  2. Liu Y, Wang Y, Zhang J. New machine learning algorithm: Random forest. In International Conference on Information Computing and Applications. Berlin, Heidelberg: Springer; 2012; 246–252.
  3. Mythili T, Mukherji D, Padalia N, Naidu A. A heart disease prediction model using SVM-decision trees-logistic regression (SDL). Int J Comput Appl Technol. 2013; 68(16): 11–15.
  4. Shah D, Patel S, Bharti SK. Heart disease prediction using machine learning techniques. SN Comput Sci. 2020; 1(6): 1–6.
  5. Ali M, Khan MD, Imran MA, Siddiki M. Heart disease prediction using machine learning algorithms. Doctoral dissertation. Bangladesh: BRAC University; 2019.
  6. Kondababu A, Siddhartha V, Kumar BB, Penumutchi B. A comparative study on machine learning based heart disease prediction. Mater Today: Proc. 2021.
  7. Bharti R, Khamparia A, Shabaz M, Dhiman G, Pande S, Singh P. Prediction of heart disease using a combination of machine learning and deep learning. Comput Intell Neurosci. 2021 Jul 1; 2021: 8387680.
  8. Sarah S, Gourisaria MK, Khare S, Das H. Heart Disease Prediction Using Core Machine Learning Techniques—A Comparative Study. In Advances in Data and Information Sciences. Singapore: Springer; 2022; 247–260.
  9. Riyaz L, Butt MA, Zaman M, Ayob O. Heart Disease Prediction Using Machine Learning Techniques: A Quantitative Review. In International Conference on Innovative Computing and Communications. Singapore: Springer; 2022; 81–94.
  10. Gao Y, Amin Ali A, Shaban Hassan H, Anwar EM. Improving the accuracy for analyzing heart diseases prediction based on the ensemble method. Complexity. 2021; 2021: 6663455.
  11. Abdullah AS, Rajalaxmi R. A data mining model for predicting the coronary heart disease using random forest classifier. In International conference in recent trends in computational methods, communication and controls. 2012 Apr; 22–25.
  12. Nikhar S, Karandikar AM. Prediction of Heart Disease Using Different Classification Techniques. Aptikom Journal on Computer Science and Information Technologies. 2017; 2(2): 68–74.
  13. Hasan R. Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction. In ITM Web of Conference, EDP Sciences. 2021; 40: 03007.
  14. Shah D. Heart Disease Prediction using Machine Learning Techniques. Singapore: Springer Nature Pte Ltd.; 2020.
  15. Singh B, Tiwari P, Singh SN, Payasi RP, Vishwakarma M, Patel DK. Effects of power factor variation of distributed generations on its size and location in power systems for enhancement of system performances. 2020 International Conference on Electrical and Electronics Engineering (ICE3), Gorakhpur, India, 2020, pp. 1-6. DOI: 10.1109/ICE348803.2020.9122829.
  16. Baral S, Satpathy S, Pati DP, Mishra P, Pattnaik L. A Literature Review for Detection and Projection of Cardiovascular Disease Using Machine Learning. EAI Endorsed Transactions on Internet of Things. 2024; 10: 1–7.
  17. Patidar S, Kumar D, Rukwal D. Comparative Analysis of Machine Learning Algorithms for Heart Disease Prediction. In Advanced Production and Industrial Engineering. IOS Press; 2022; 1–8.
  18. Lapp D. (2019). Heart Disease Dataset. [Online]. Kaggle.com. Available from: https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset
  19. Singh IV, Singh R, Singh A, Singh P. Analyzing the cognitive proficiencies of artificial intelligence within the legal paradigm: prospects within the jurisdiction of India. J Artif Intell Res Adv. 2024;11(01):84-112.

Regular Issue Subscription Review Article
Volume 11
Issue 02
Received 09/07/2024
Accepted 17/07/2024
Published 22/07/2024


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