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

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Sachin Sahu, Zuber Farooqui

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

	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 = {},
	doi = {10.5120/ijca2021921201},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


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.


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