[{“box”:0,”content”:”[if 992 equals=”Open Access”]n
n
Open Access
nn
n
n[/if 992]n
n
n
n
n
n
Shivam Saini, Ujjwal Thakur, Shashwat Verma, Anand Sehgal,
n
- n t
n
n
n[/foreach]
n
n[if 2099 not_equal=”Yes”]n
- [foreach 286] [if 1175 not_equal=””]n t
- Student, Student, student, Assistant professor Department of computer science and engineering, The Northcap University, Gurgaon, Department of computer science and engineering, The Northcap University, Gurgaon, Department of computer science and engineering, The Northcap University, Gurgaon, Department of computer science and engineering, The Northcap University, Gurgaon Haryana, Haryana, Haryana, Haryana India, India, India, India
n[/if 1175][/foreach]
n[/if 2099][if 2099 equals=”Yes”][/if 2099]n
Abstract
nThe present investigation looks at how well various machine learning algorithms predict cardiac disease. Since heart disease is one of the major causes of death worldwide, early detection and precise diagnosis are essential for managing and treating the condition. Our goal is to enhance diagnostic processes and improve patient outcomes by leveraging machine learning techniques. Six widely-used machine learning algorithms are evaluated in this research paper. These algorithms were selected due to their established effectiveness in classification tasks. Numerous patient characteristics, including age, gender, blood pressure, cholesterol, and other pertinent medical data, are included in the dataset. To evaluate each algorithm’s performance, we separated the dataset into subsets for testing and training. Our study’s primary goal is to evaluate each algorithm’s predictive power for heart disease. Various performance indicators are employed for both the training and testing datasets, such as accuracy, precision, recall, and F-measure. These metrics give a comprehensive view of each model’s predictive capabilities and how well they generalize. This research has significant implications for the use of machine learning in healthcare. By identifying the strengths and weaknesses of different algorithms in predicting heart disease, we provide valuable insights that can help develop more reliable and accurate prediction models. This study not only broadens our understanding of machine learning applications in healthcare but also sets the stage for future research aimed at improving patient care through advanced predictive analytics. Our study underscores the potential of machine learning to revolutionize diagnostic processes and improve outcomes for patients at risk of heart disease. By using these advanced algorithms, we can move towards more effective and personalized healthcare solutions.
n
Keywords: Machine learning, heart disease, neural network, Kaggle, evaluation
n[if 424 equals=”Regular Issue”][This article belongs to Trends in Mechanical Engineering & Technology(tmet)]
n
n
n
n
n
nn[if 992 equals=”Open Access”] Full Text PDF Download[/if 992] n
nn[if 379 not_equal=””]n
Browse Figures
n
n
n[/if 379]n
References
n[if 1104 equals=””]n
- World Health Organization. (2020). Cardiovascular diseases (CVDs). Retrieved from https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).
- S. Virani, A. Alonso, H. J. Aparicio, E. J. Benjamin, M. S. Bittencourt, C. W. Callaway, A. P. Carson, A. M. Chamberlain, S. Cheng, F. N. Delling, M. S. V. Elkind, K.R. Evenson, J.F. Ferguson, D. K. Gupta, S. S. Khan, B. M. Kissela, K. L. Knutson, C. D. Lee, & C. W. Tsao (2020). Heart disease and stroke statistics—2020 update: A report from the American Heart Association. Circulation, 141(9), e139–e596.
- I. Attia, S. Kapa, F. Lopez-Jimenez, P.M. McKie, D.J. Ladewig, G. Satam, P.A. Pellikka, M. Enriquez-Sarano, P.A. Noseworthy, & T.M. Munger, (2019). Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nature Medicine, 25(1), 70–74.
- Krittanawong, H. Zhang, Z. Wang, M. Aydar, & T. Kitai, T. (2018). Artificial intelligence in precision cardiovascular medicine. Journal of the American College of Cardiology, 71(23), 2668–2679.
- Dey, P. J. Slomka, P. Leeson, D. Comaniciu, S. Shrestha, P.P. Sengupta, T.H. Marwick, A. Banerjee, J. K. Min, & Y. Chandrashekhar (2019). Artificial intelligence in cardiovascular imaging: JACC state-of-the-art review. Journal of the American College of Cardiology, 73(11), 1317–1335.
- Motwani, D. Dey, D. S. Berman, G. Germano, S. Achenbach, M. H. Al-Mallah, D. Andreini, M. J. Budoff, F. Cademartiri, T. Q. Callister, H. J. Chang, K. Chinnaiyan, B. J. Chow, R. C. Cury, A. Delago, M. Gomez, H. Gransar, M. Hadamitzky, J. Hausleiter, & P. J. Slomka (2020). Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: A 5-year multicentre prospective registry analysis. European Heart Journal, 41(25), 4428–4437.
- F. Weng, J. Reps, J. Kai, J. M. Garibaldi, & N. Qureshi (2017). Can machine learning improve cardiovascular risk prediction using routine clinical data? PLOS ONE, 12(4), e0174944.
- Amarbayasgalan, V.-H. Pham, N. Theera-Umpon, Y. Piao, & K. H. Ryu (2021). An Efficient Prediction Method for Coronary Heart Disease Risk Based on Two Deep Neural Networks Trained on Well-Ordered Training Datasets. IEEE, 135210 – 135223.
- Raval, J.P. Verma, S.N.M. Islam, R. Jain, N. Thakur (2022). AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach. IEEE.
- Gavhane, G Kokkula, I Pandya and K Devadkar, “Prediction of heart disease using machine learning in: 2018 second international conference on electronics communication and aerospace technology (ICECA)”, IEEE, pp. 1275-1278, 2018.
- https://towardsdatascience.com/heart-disease-prediction-73468d630cfc
- https://www.analyticsvidhya.com/blog/2022/02/heart-disease-prediction-using-machine-learning-2/
nn[/if 1104][if 1104 not_equal=””]n
- [foreach 1102]n t
- [if 1106 equals=””], [/if 1106][if 1106 not_equal=””],[/if 1106]
n[/foreach]
n[/if 1104]
nn
nn[if 1114 equals=”Yes”]n
n[/if 1114]
n
n
n
n
n
nnn
n
Volume | 14 | |
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
Received | May 19, 2024 | |
Accepted | July 2, 2024 | |
Published | August 23, 2024 |
n
n
n
n
n
n nfunction myFunction2() {nvar x = document.getElementById(“browsefigure”);nif (x.style.display === “block”) {nx.style.display = “none”;n}nelse { x.style.display = “Block”; }n}ndocument.querySelector(“.prevBtn”).addEventListener(“click”, () => {nchangeSlides(-1);n});ndocument.querySelector(“.nextBtn”).addEventListener(“click”, () => {nchangeSlides(1);n});nvar slideIndex = 1;nshowSlides(slideIndex);nfunction changeSlides(n) {nshowSlides((slideIndex += n));n}nfunction currentSlide(n) {nshowSlides((slideIndex = n));n}nfunction showSlides(n) {nvar i;nvar slides = document.getElementsByClassName(“Slide”);nvar dots = document.getElementsByClassName(“Navdot”);nif (n > slides.length) { slideIndex = 1; }nif (n (item.style.display = “none”));nArray.from(dots).forEach(nitem => (item.className = item.className.replace(” selected”, “”))n);nslides[slideIndex – 1].style.display = “block”;ndots[slideIndex – 1].className += ” selected”;n}n”}]