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Heart Disease Prediction using Machine Learning

Author(s):

Rohit Saroha , ARMY INSTITUTE OF TECHNOLOGY PUNE; Prof. Geeta Patil, ARMY INSTITUTE OF TECHNOLOGY PUNE; Manish Kumar, ARMY INSTITUTE OF TECHNOLOGY PUNE; Vineet Kumar, ARMY INSTITUTE OF TECHNOLOGY PUNE; Mohd Anwar Hussain, ARMY INSTITUTE OF TECHNOLOGY PUNE

Keywords:

Heart Disease, Artificial Neural Network, Multilayer Perceptron, Genetic Algorithm, Back-Propagation Algorithm

Abstract

The majority of the passings over the globe are brought about by heart maladies and it is a standout amongst the most fatal ailment turning into the purpose behind a large portion of the deaths. Many calculations are being connected to identify the heart illnesses before they achieve a phase at which it can’t be restored. Building up a restorative conclusion framework dependent on AI for forecast of coronary illness gives more precise analysis than customary way. This paper exhibits a review of backpropagation exactness and backpropagation alongside the hereditary calculations precision and break down their performance. In this paper, a coronary illness expectation framework which utilizes counterfeit neural system back propagation calculation is proposed. Thirteen clinical parameter[2] were utilized as contribution for the neural system and afterward the neural system was prepared with backpropagation calculation alongside the hereditary calculations to foresee non-appearance or nearness of coronary illness with precision of 92.

Other Details

Paper ID: IJSRDV7I30552
Published in: Volume : 7, Issue : 3
Publication Date: 01/06/2019
Page(s): 748-751

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