HEART DISEASE PREDICTION USING MACHINE LEARNING
DOI:
https://doi.org/10.61841/khan0t70Keywords:
—Machine learning, Heart disease,, Heart Sounds,, machine learning algorithms, Heart disease prediction.Abstract
There are many deadly diseases present in the huge population of the people in the world, Heart diseases are some of them. When death rates are considered it is known that many p e o p l e a r e su f f e r i ng from hear diseases since these diseases are very dangerous early diagnosis is very important. The major cause of death is Heart diseases according to WHO (World Health Organization). Using a standard method for diagnosis is not good enough fo these diseases. We need to develop a good medical diagnosis model which uses machine learning algorithms and techniques for prediction of diseases an h i s g i v e s an accurate diagnosis and results than the standard method. By predicting the disease in an earlier stage it reduces the cost of treatment and it also plays an essential role in the treatment. This prediction system’s application is to take the input of the patient’s data and heartbeat sound recordings and predict the diseases.
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References
1. A Tülay Karaylan, “Prediction of Heart Disease Using Neural Network,” 2nd International Conference on Computer Science and Engineering. (2017)
2. A V.V Ramalingam, “Heart disease prediction using machine learning,” from Science pubco. (2018)
3. A Sanjay Kumar Sen, “Predicting and Diagnosing of Heart Disease Using Machine Learning,” International Journal of Engineering and Computer Science. (2017)
4. A research citation by Animesh Hazra, Subrata Kumar Mandal from research publications India. (2017)
5. A Mohan Mishra, Anushikha Singh, Malay Kishore Dutta, “Classification of Normal and Abnormal Heat Sounds for Automatic Diagnosis” IEEE. (2017)
6. A Peter Bentley, Glenn Nordehn, Miguel Coimbra, Shie Mannor, RitaGetz. (2017)
7. A Anatoly Yakovlev, “Application of Machine Learning Techniques for Heart Sound Recording Classification”, 2017 Stanford, Vincent Lee. (2017)
8. A Purushottama, Prof. (Dr.) Kanak Saxenab, Richa Sharma, “Efficient
9. Heart Disease Prediction System”, science direct. (2016)
10. A Tanvi Sharma, Sahil Verma, Kavita, “Intelligent Heart DiseasePrediction System using Machine Learning”, IJRA. (2017)
11. A Amin Ul Haq, Jian Ping Li, Muhammad Hammad Memon, “A Hybrid Intelligent System Framework for the Prediction of Heart Disease Using Machine Learning Algorithms”, Hindawi. (2018)
12. A Jayshril S. Sonawanel, D. R. PatiI, “Prediction of Heart DiseaseUsing Learning Vector Quantization
Algorithm”, IEEE. (2014)
13. A Jayshril S. Sonawane, D. R. Patil, “Prediction of Heart Disease Using
14. Multilayer Perceptron Neural Network”, IEEE. (2014)
15. A Mrs. S. Radhimeenakshi, “Classification and Prediction of Heart Disease Risks using Data Mining Techniques of Support Vector Machine and Artifical neural network”, IEEE. (2016)
16. A Jagdeep Singh, Amit Kamra, Harbhag Singh, “Prediction of Heart
17. Diseases Using Associative Classification”, IEEE. (2016)
18. A Kaan Uyara, Ahmet Ilhan, “Diagnosis of heart disease using genetic algorithm based trained recurrent fuzzy neural networks”, Science Direct. (2017) A S.M. DEBBAL, F. BEREKSI-REGUIG, “Frequency analysis of the heartbeat sounds”, IJBSCHS. (2014)
19. A Koredianto Usman, Muhammad Athar Sadiq, Hiroshi Juzoji, and Isao Nakajima, “A Study of Heartbeat Sound Separation Using Independent Component Analysis Technique”, IEEE. (2004)
20. A E Safar Khorasani, S Doraisamy, A Azman, M Azmi Murad, “An Approach towards a Heartbeat Sound Information Retrieval System”, ISSN 0276−6574. (2010)
21. A Amir Mohammad Amiri, Giuliano Armano, “Heart Sound Analysis for Diagnosis of Heart Diseases in Newborns”, Science Direct. (2013)
22. A Simarjot kaur Rhandhawa, and Mandeep Singh, “Classification of heart sound signals using Multi – modal features”, Science Direct. (2015)
23. A Animesh Hazra, Subrata Kumar Mandal, Amit Gupta, Arkomita Mukherjee and Asmita Mukherjee, “Heart Disease Diagnosis and Prediction Using Machine Learning and Data Mining Techniques: A Review”, ISSN 0973-6107 Volume 10, Number 7. (2017
24. https://en.wikipedia.org/wiki/Random_forest.
25. https://en.wikipedia.org/wiki/Weka_(machine_learning) https://en.wikipedia.org/wiki/MATLAB.
26. https://en.wikipedia.org/wiki/R_(programming_language)
27. https://machinelearningmastery.com/machine-learning-in-python-step-by-step/“Index of /ml/machine- learning-databases,” Index of/ml/machine- learning-databases. [Online]. Available: http://archive.ics.uci.edu/ml/machine- learning-databases/ heart- disease/heart-disease.names. [Accessed: 03-Jul- 2017].
28. G. D. Clifford et al., "Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016," 2016 Computing in Cardiology Conference (CinC), Vancouver, BC,2016, pp. 609-612. http://ieeexplore.ieee.org/document/7868816/Dataset URL: https://physionet.org/challenge/2016/
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