International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

Building a Classifier using Random Forests

Shah Sanika, Pradhan Madhavi

Abstract: In the healthcare industry, the data is growing day by day. Many organizations store the records of patients in the form of electronic healthcare records (EHR). These records can be used by the doctor for tasks like diagnosis of diseases, etc. Also to detect a disease early, doctor may use the EHR. To assist the doctor, if an algorithm is provided, which classifies data, it becomes much easier for the doctor to identify patients who are likely to develop a disease. Thus, early detection is one advantage of building such classifiers. Another advantage is that the cost of treating any disease will be reduced. Classifier will help to predict the disease and aid in diagnosis. Here, we propose a classifier which gives predictions once the training has been done.

Keywords: Classifier, random forests, evolutionary computing

How to Cite?: Shah Sanika, Pradhan Madhavi, "Building a Classifier using Random Forests", Volume 4 Issue 7, July 2015, International Journal of Science and Research (IJSR), Pages: 2412-2414, https://www.ijsr.net/getabstract.php?paperid=SUB157057, DOI: https://dx.doi.org/10.21275/SUB157057

Download Citation: APA | MLA | BibTeX | EndNote | RefMan

Share This Research

Help this article reach readers, researchers and professionals.

Share activity is measured for research-engagement analytics. Only verified, unique public shares can support award tie-breaking.

Confirm Your Share

Enter your details so IJSR can confirm this sharing activity.

Your details are used to validate this share and protect the award process from duplicate or false activity.

Download Article PDF


Rate This Article!

Top

Confirm Your Share

Enter your details so IJSR can confirm this sharing activity.

Your details are used to validate this share and protect the award process from duplicate or false activity.