An Efficient Statistical Computation Technique for Health Care Big Data using R

International Conference on Materials, Alloys and Experimental Mechanics 2017

材料科学;金属学;机械制造

Rani, Sushma N.^1 ; Rao, P. Srinivasa^1 ; Parimala, P.^1

Computer Science and Engineering Department, M.V.G.R College of Engineering, India^1

关键词: Accuracy;    Breast Cancer;    Classification evaluation;    Health-related problems;    Instance based learning;    K nearest neighbor (KNN);    K-nearest neighbors;    Statistical computations;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/225/1/012159/pdf
DOI  :  10.1088/1757-899X/225/1/012159

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Due to the changes in living conditions and other factors many critical health related problems are arising. The diagnosis of the problem at earlier stages will increase the chances of survival and fast recovery.This reduces the time of recovery and the cost associated for the treatment. One such medical related issue is cancer and breast cancer has been identified as the second leading cause of cancer death. If detected in the early stage it can be cured. Once a patient is detected with breast cancer tumor, it should be classified whether it is cancerous or non-cancerous. So the paper uses k-nearest neighbors(KNN) algorithm which is one of the simplest machine learning algorithms and is an instance-based learning algorithm to classify the data. Day- to -day new records are added which leds to increase in the data to be classified and this tends to be big data problem. The algorithm is implemented in R which is the most popular platform applied to machine learning algorithms for statistical computing. Experimentation is conducted by using various classification evaluation metric on various values of k.The results show that the KNN algorithm out performes better than existing models.