TY  - JOUR
T1  - Pragmatic Assessment of Occurrence of Brain Cancer with Incidence Levels using
Collaborative Big Data Mining Techniques
AU - Rizwan, Syed AU - Madhav Kuthadi, Venu AU - Selvaraj, Rajalakshmi 
JO  - International Journal of Soft Computing
VL  - 14
IS  - 3
SP  - 61
EP  - 67
PY  - 2019
DA  - 2001/08/19
SN  - 1816-9503
DO  - ijscomp.2019.61.67
UR  - https://makhillpublications.co/view-article.php?doi=ijscomp.2019.61.67
KW  - Brain cancer
KW  -incidence levels
KW  -big data mining
KW  -stages of brain cancer
KW  -k-means clustering
KW  -big data analytics
AB  - The major objective of this research is to
identify the presence of brain cancer along with the
incidence levels of beginning stage to advanced stage
using collaborative analysis of big data and data mining
techniques. The dataset collected from secondary sources
had few errors and rectified using preprocessing
techniques in MATLAB. Further, the testing dataset is
processed with k-means algorithm to form cluster analysis
and identify the presence of brain cancer in three levels of
well, fair and poor levels using degree of difference
between the normal and cancer cells in brain. The
algorithm is modified according to the needs of the
medical analysis of the current dataset. The results
indicates the presence of brain cancer in various three
levels under cluster values of initial stage (54%), Curable
stage (38%) and incurable stage (8%), respectively. The
accuracy of prediction is 93.4% and the error
identification is 9.3% whereas the sensitivity and
specificity accounts to 0.8 and 0.7, respectively. Hence,
further analysis is conducted in tableau big data tool and
the sheets with story boards are formed. This research
indicates the occurrence of brain cancer is influenced by
gender and age factors along with regular activities and
streams. Thus, brain cancer is considered as one of the
challenging prediction as the cell contains mixed patterns
with variations according to gender and age of human
beings.
ER  -