Call for Paper - January 2023 Edition
IJCA solicits original research papers for the January 2023 Edition. Last date of manuscript submission is December 20, 2022. Read More

High Accurancy and Low Risk Prediction and Diagnosis Heart Disease using Gradient Boosting Algorithm

Print
PDF
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Authors:
Sachin Sahu, Zuber Farooqui
10.5120/ijca2021921201

Sachin Sahu and Zuber Farooqui. High Accurancy and Low Risk Prediction and Diagnosis Heart Disease using Gradient Boosting Algorithm. International Journal of Computer Applications 174(28):25-28, April 2021. BibTeX

@article{10.5120/ijca2021921201,
	author = {Sachin Sahu and Zuber Farooqui},
	title = {High Accurancy and Low Risk Prediction and Diagnosis Heart Disease using Gradient Boosting Algorithm},
	journal = {International Journal of Computer Applications},
	issue_date = {April 2021},
	volume = {174},
	number = {28},
	month = {Apr},
	year = {2021},
	issn = {0975-8887},
	pages = {25-28},
	numpages = {4},
	url = {http://www.ijcaonline.org/archives/volume174/number28/31854-2021921201},
	doi = {10.5120/ijca2021921201},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

This paper gives an endeavor to productively arrange and foresee heart illnesses at a beginning phase with high exactness and execution measures. The huge commitment of this exposition is isolated into two sections. Initial, a powerful way to deal with prior location and grouping of coronary illness is portrayed. Next, a fourier change based clinical proposal model is introduced for the previous conclusion of heart diesease. Supervised machine learning classifiers can be categorized into multiple types. These types include naïve Bayes, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA), generalized linear models, stochastic gradient descent, support vector machine (SVM), linear support vector classifier (Linear SVC) decision trees, neural network models, nearest neighbours and ensemble methods. The ensemble methods combine weak learners to create strong learners. In this paper the implemented result with the help of gradient boosting algorithms.

References

  1. M.Ganesan and Dr. N. Sivakumar, “IoT based heart disease prediction and diagnosis model for healthcare using machine learning models”, International Conference on System, Computation, Automation and Networking (ICSCAN), IEEE 2019.
  2. Priyan Malarvizhi Kumar, Usha Devi Gandhi, "A novel threetier Internet of Thingsnarchitecture with machine learning algorithm for early detection of heart diseases", Computers and Electrical Engineering, Vol.65, pp. 222–235, 2018.
  3. Prabal Verma, Sandeep K. Sood, "Cloud-centric IoT based disease diagnosis healthcare framework", J, Parrallel Distrib. Comput., 2018.
  4. M.Ganesan, Dr.N.Sivakumar, “A Survey on IoTrelated Patterns”,International Journal of Pure and Applied Mathematics,Volume 117 No. 19, 365-369, 2017.
  5. R.Rajaduari, M.Ganesan,Ms.Nithya “A Survey on Structural Health Monitoring based on Internet of Things”International Journal of Pure and Applied Mathematics,Volume 117 No. 18, 389-393, 2017.
  6. Amin Khatami, AbbasKhosravi, C. L. (2017), ‘Medical image analysis using wavelet transform and deep belief networks’, Journal of Expert Systems With Applications 3(4), 190–198.
  7. Zhang, Shuai, Y.-L. S. A. (2017), ‘Deep learning based recommender system: a survey and new perspectives’, Journal of ACM Computing Surveys 1(1), 1–35.
  8. Zhiyong Wang, Xinfeng Liu, J. G. (2016), ‘Identification of metabolic biomarkers in patients with type-2 diabetic coronary heart diseases based on metabolomic approach’, 6(30), 435–439.
  9. Ashwini Shetty, Naik, C. (2016), ‘Different data mining approaches for predicting heart disease’, International journal of innovative research in science, engineering and technology 3(2), 277–281.
  10. Berikol, B. and Yildiz (2016), ‘Diagnosis of acute coronary syndrome with a support vector machine’, Journal of Medical System 40(4), 11–18.
  11. Chebbi, A. (2016), ‘Heart disease prediction using data mining techniques’, International journal of research in advent technology 25(3), 781–794.
  12. Cheng-Hsiung Wenga, Tony Cheng-Kui Huang, R.-P. H. (2016), ‘Disease prediction with different types of neural network classifiers’, Journal of Telematics and Informatics (4), 277–292.
  13. Ghadge, Prajakta, K. (2016), ‘Intelligent heart attack prediction system using big data’, International journal of recent research in mathematics computer science and information technology 2(2), 73–77.
  14. Lafta, R., Zhang, J. and Tao (2016), ‘An intelligent recommender system based on short-term risk prediction for heart disease patients’, Journal of web intelligence and intelligent agent technology (12), 102–105.

Keywords

Gradient Boosing, Support Vector Machine, Neural Network, Classification, Heart Disease