|
INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
Imp Links for Author
Imp Links for Reviewer
Research Area
Subscribe IJSDR
Visitor Counter
Copyright Infringement Claims
Indexing Partner
|
Published Paper Details
|
|
| Paper Title: | A DIAGNOSIS OF HEART DISEASE USING MACHINE LEARNING |
| Authors Name: | PADI PRASANNAKUMAR , N. BHARGAV REDDY , V. DORABABU , N. JESWANTH , DR. ABRAHAM MATHEW |
| Unique Id: | IJSDR2304037 |
| Published In: | Volume 8 Issue 4, April-2023 |
| Abstract: | Day by day the cases of heart diseases are increasing at a rapid rate and it’s very Important and concerning to predict any such diseases beforehand. This diagnosis is a difficult task i.e. it should be performed precisely and efficiently. The research paper mainly focuses on which patient is more likely to have a heart disease based on various medical attributes. We prepared a heart disease prediction system to predict whether the patient is likely to be diagnosed with a heart disease or not using the medical history of the patient. We used different algorithms of machine learning such as logistic regression and SVM to predict and classify the patient with heart disease. A quite Helpful approach was used to regulate how the model can be used to improve the accuracy of prediction of Heart Attack in any individual. The strength of the proposed model was quiet satisfying and was able to predict evidence of having a heart disease in a particular individual by using SVM, KNN, Decision Tree Classifier, Random Forest Classifier, Naïve Bayes and Logistic Regression which is used to predict the model with above Machine Learning Algorithms. Among the above the SVM showed a good accuracy in comparison to the previously used classifiers.So a quiet significant amount of pressure has been lift off by using the given model in finding the probability of the classifier to correctly and accurately identify the heart disease. The given heart disease prediction system enhances medical care and reduces the cost.This project gives us significant knowledge that can help us predict the patients with heart disease it is implemented on the. pynb format. |
| Keywords: | Prediction, Heart Disease, Symptoms, Machine Learning |
| Cite Article: | "A DIAGNOSIS OF HEART DISEASE USING MACHINE LEARNING", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 4, page no.191 - 195, April-2023, Available :http://www.ijsdr.org/papers/IJSDR2304037.pdf |
| Downloads: | 000337214 |
| Publication Details: | Published Paper ID: IJSDR2304037 Registration ID:205069 Published In: Volume 8 Issue 4, April-2023 DOI (Digital Object Identifier): Page No: 191 - 195 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijsdr.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
Track Paper
Important Links
Conference Proposal
ISSN
 |
 |
DOI (A digital object identifier)
  Providing A digital object identifier by DOI How to GET DOI and Hard Copy Related |
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
 |
 |
 |
Social Media
Indexing Partner |
|||
| Copyright © 2024 - All Rights Reserved - IJSDR | |||
Facebook Twitter Instagram LinkedIn