Machine Learning Algorithms for Heart Disease Prediction
Cardiovascular disease, otherwise known as heart disease, encompasses many diseases that affect the heart. Heart disease prediction is among the most complicated tasks in medical field. In the modern age, about one person dies every minute as a result of heart disease. In addition to many factors that contribute to heart disease, it's necessary at this point in time to acquire accurate, reliable, and sensible approaches to make an early diagnosis so that the disease may be managed appropriately. Due to the complexity of finding out the heart condition, the prediction process must be automated to avoid risks related to it and to alert the patient at an early stage. In the healthcare domain, data mining is commonly used to analyze huge, complex medical data and predict heart disease. Researchers apply a variety of data mining and machine learning approaches to analyse huge complex medical data and predict heart disease. In this study, various heart disease attribute are presented, and model is developed on the basis of supervised learning algorithm as K-nearest neighbor, Decision Tree, Random Forest, Logistic Regression, SVM, Light GBM and Naïve bayes. This Paper makes use of heart condition dataset available in Kaggle repository. The purpose of this study is to anticipate heart disease risk in patients. The results show that K-nearest neighbor provides the most accurate result.
 T.Nagamani, S.Logeswari, B.Gomathy,” Heart Disease Prediction using Data Mining with Mapreduce Algorithm”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-3, January 2019.
 Fahd Saleh Alotaibi,” Implementation of Machine Learning Model to predict Heart Failure Disease”, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 10, No. 6, 2019. Asian Journal of Convergence in Technology
ISSN NO: 2350-1146 I.F-5.11
Volume VIII and Issue I
 Anjan Nikhil Repaka, Sai Deepak Ravikanti, Ramya G Franklin, ”Design And Implementation Heart Disease Prediction Using Naives Bayesian”, International Conference on Trends in Electronics and Information(ICOEI 2019).
 UCI, ―Heart Disease Data Set.[Online]. Available (Accessed on May 1 2020): https://www.kaggle.com/ronitf/heart-disease-uci.
 Theresa Princy R,J. Thomas,’Human heart Disease Prediction System using Data Mining Techniques’, International Conference on Circuit Power and Computing Technologies,Bangalore,2016.
 Nagaraj M Lutimath,Chethan C,Basavaraj S Pol.,’Prediction Of Heart Disease using Machine Learning’, International journal Of Recent Technology and Engineering,8,(2S10), pp 474-477, 2019.
 C. B. Rjeily, G. Badr, E. Hassani, A. H., and E. Andres, ―Medical Data Mining for Heart Diseases and the Future of Sequential Mining in Medical Field,‖ in Machine Learning Paradigms, 2019, pp. 71–99.
 Fajr Ibrahem Alarsan., and Mamoon Younes ‘Analysis and classification of heart diseases using heartbeat features and machine learning algorithms’,Journal Of Big Data,2019;6:81.
 Internet source [Online].Available (Accessed on May 1 2021): http://acadpubl.eu/ap.
 Jafar Alzubi, Anand Nayyar, Akshi Kumar. "Machine Learning from Theory to Algorithms: An Overview", Journal of Physics: Conference Series, 2018.
 Sayali Ambekar, Rashmi Phalnikar,“Disease Risk Prediction by Using Convolutional Neural Network”,2018 Fourth International Conference on Computing Communication Control and Automation.
To ensure uniformity of treatment among all contributors, other forms may not be substituted for this form, nor may any wording of the form be changed. This form is intended for original material submitted to AJCT and must accompany any such material in order to be published by AJCT. Please read the form carefully.
The undersigned hereby assigns to the Asian Journal of Convergence in Technology Issues ("AJCT") all rights under copyright that may exist in and to the above Work, any revised or expanded derivative works submitted to AJCT by the undersigned based on the Work, and any associated written, audio and/or visual presentations or other enhancements accompanying the Work. The undersigned hereby warrants that the Work is original and that he/she is the author of the Work; to the extent the Work incorporates text passages, figures, data or other material from the works of others, the undersigned has obtained any necessary permission. See Retained Rights, below.
AJCT distributes its technical publications throughout the world and wants to ensure that the material submitted to its publications is properly available to the readership of those publications. Authors must ensure that The Work is their own and is original. It is the responsibility of the authors, not AJCT, to determine whether disclosure of their material requires the prior consent of other parties and, if so, to obtain it.
RETAINED RIGHTS/TERMS AND CONDITIONS
1. Authors/employers retain all proprietary rights in any process, procedure, or article of manufacture described in the Work.
2. Authors/employers may reproduce or authorize others to reproduce The Work and for the author's personal use or for company or organizational use, provided that the source and any AJCT copyright notice are indicated, the copies are not used in any way that implies AJCT endorsement of a product or service of any employer, and the copies themselves are not offered for sale.
3. Authors/employers may make limited distribution of all or portions of the Work prior to publication if they inform AJCT in advance of the nature and extent of such limited distribution.
4. For all uses not covered by items 2 and 3, authors/employers must request permission from AJCT.
5. Although authors are permitted to re-use all or portions of the Work in other works, this does not include granting third-party requests for reprinting, republishing, or other types of re-use.
INFORMATION FOR AUTHORS
AJCT Copyright Ownership
It is the formal policy of AJCT to own the copyrights to all copyrightable material in its technical publications and to the individual contributions contained therein, in order to protect the interests of AJCT, its authors and their employers, and, at the same time, to facilitate the appropriate re-use of this material by others.
If you are employed and prepared the Work on a subject within the scope of your employment, the copyright in the Work belongs to your employer as a work-for-hire. In that case, AJCT assumes that when you sign this Form, you are authorized to do so by your employer and that your employer has consented to the transfer of copyright, to the representation and warranty of publication rights, and to all other terms and conditions of this Form. If such authorization and consent has not been given to you, an authorized representative of your employer should sign this Form as the Author.
AJCT requires that the consent of the first-named author and employer be sought as a condition to granting reprint or republication rights to others or for permitting use of a Work for promotion or marketing purposes.
1. The undersigned represents that he/she has the power and authority to make and execute this assignment.
2. The undersigned agrees to indemnify and hold harmless AJCT from any damage or expense that may arise in the event of a breach of any of the warranties set forth above.
3. In the event the above work is accepted and published by AJCT and consequently withdrawn by the author(s), the foregoing copyright transfer shall become null and void and all materials embodying the Work submitted to AJCT will be destroyed.
4. For jointly authored Works, all joint authors should sign, or one of the authors should sign as authorized agent
for the others.
Licenced by :