An Intelligent Cardiac Ailment Prediction Using Efficient ROCK Algorithm and K- Means & C4.5 Algorithm

Yusuf Perwej, Firoj Parwej and Nikhat Akhtar
Additional contact information
Yusuf Perwej: Assistant Professor, Department of Information Technology, Al Baha University, Al Baha, Kingdom of Saudi Arabia (KSA)

European Journal of Engineering and Technology Research, 2018, vol. 3, issue 12, 126-134

Abstract: The data mining techniques have the ability to discover hidden patterns or correlation among the objects in the medical data. There are many areas that adapt data mining techniques, namely marketing, stock, health care sector and so on. In the health care industry produces gigantic quantities of data that clutches complex information relating to the sick person and their medical conditions. The data mining has an infinite potential to make use of healthcare data more effectually and efficiently to predict various kinds of disease. The present-time healthcare industry heart ailment is a term that assigns to an enormous number of health care circumstances related to heart. These medical circumstances relate to the unexpected health circumstance that straight control the cardiac. In this paper we are using a ROCK algorithm because it uses Jaccard coefficient on the contrary using the distance measures to find the similarity between the data or documents to classify the clusters and the contrivance for classifying the clusters based on the similarity measure shall be used over a given set of data. Afterward, C4.5 algorithm is used as the training algorithm to show the rank of a cardiac ailment with the decision tree. The C4.5 can be referred as the statistic classifier as well as this algorithm uses avail radio for feature selection and to build the decision tree. The C4.5 algorithm is widely used because of its expeditious classification and high exactitude. Lastly, the cardiac ailment database is clustered using the K-means clustering, which will alienate the data convenient to cardiac sickness from the database.

Keywords: Data Mining; ROCK; C4.5 Algorithm; K-Means; Clustering; Medical Dataset. (search for similar items in EconPapers)
Date: 2018
References: Add references at CitEc
Citations:

Downloads: (external link)
https://eu-opensci.org/index.php/ejeng/article/view/60989 Abstract page (text/html)
https://eu-opensci.org/index.php/ejeng/article/download/60989/11995 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:epw:ejeng0:v:3:y:2018:i:12:id:60989

DOI: 10.24018/ejeng.2018.3.12.989

Access Statistics for this article

More articles in European Journal of Engineering and Technology Research from European Open Science
Bibliographic data for series maintained by Support ().