Cardiovascular Disease Prediction using Classifier algorithm
Ankur Sharma
, Neha Arora
CVD, packages, missmap, age, TenYearCHD etc.
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.
"Cardiovascular Disease Prediction using Classifier algorithm", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.5, Issue 4, page no.393 - 405, April-2020, Available :https://ijsdr.org/papers/IJSDR2004071.pdf
Volume 5
Issue 4,
April-2020
Pages : 393 - 405
Paper Reg. ID: IJSDR_191668
Published Paper Id: IJSDR2004071
Downloads: 000347210
Research Area: Engineering
Country: AJMER, Rajasthan, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
