|
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: | Cardiovascular Disease Prediction using Classifier algorithm |
| Authors Name: | Ankur Sharma , Neha Arora |
| Unique Id: | IJSDR2004071 |
| Published In: | Volume 5 Issue 4, April-2020 |
| Abstract: | Machine learning has introduced new and easiest ways of solving problems in data science. One common application of machine learning is the prediction of an outcome based upon existing data. The machine learns the data in the form of patterns and then applies them in order to predict an outcome. This paper searches a method termed ensemble classification, which is used for improving the accuracy of weak algorithm. R programming is used in order to visualise and analyse the data and to make prediction on the outcome. In this paper, research is done to apply the properties of machine learning in healthcare sector. Cardiovascular Disease or Heart Disease is one of the most common and deadliest diseases now-a-days. There are various parameters that results in such kind of disease. In today’s world, data is the major entity which defines one’s characteristics/identity whether it’s a name, age etc. Similarly, in healthcare doctor diagnose the patients and keep a track of their record in a form of datasets. This vast data can be sort into the necessary data which is required to a doctor. Here comes the role of Big data analytics, it helps the user to visualize the data which is relevant to use and sort it down. Then the data can be analysed using classifiers. And as we all know Technology is booming day-by-day, which is resulting not only in making the work easier but also effective at the same time. This paper is focused onto the situation of predicting the rate of heart attack in a patient by visualizing and analysing their dataset. The data plays an important role in analytics, if the data is not refined and has flaws in it the accuracy rate will be much lower. This idea is based on providing a structured base to the doctors to predict the chances of CVD in a patient. By examining the social determinants of health for instance, smoking, education etc. we (doctors) would be able to predict more accurate results. The visualization technique will help the doctors to distinguish between the factors more easily and effectively, it will also help in determining the prevail and the least measure/factor for the variation in the data. As we know Big data is known for its valuable use of data controlling, handling, contrasting, managing the large dataset which gives more accurate results through analysing the whole data. The prediction rate depends upon the structure of the data i.e. if the data is unstructured then there is possibility of least accurate results. This paper illustrates the way how the Big data can be used in a significant way to predict the rate of heart attack in people through analysing their daily routine or habits and their way of understanding. This study describes the use of Framingham dataset study in which the parameters such as CurrentSmoker, Blood pressure, Diabetes, Cholesterol etc. are measured to fetch out the most possible accurate results. Framingham heart study allows us to understand the factors responsible for such heart disease. Researches are still going on for this topic to create a possible accurate model to predict CVD in people. |
| Keywords: | CVD, packages, missmap, age, TenYearCHD etc. |
| Cite Article: | "Cardiovascular Disease Prediction using Classifier algorithm", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.5, Issue 4, page no.393 - 405, April-2020, Available :http://www.ijsdr.org/papers/IJSDR2004071.pdf |
| Downloads: | 000337348 |
| Publication Details: | Published Paper ID: IJSDR2004071 Registration ID:191668 Published In: Volume 5 Issue 4, April-2020 DOI (Digital Object Identifier): Page No: 393 - 405 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