• HARMEET KAUR
• JASLEEN KAUR

The rapid increase in the Neuro diseases has significantly increased the brain tumor issue to human beings. This work focuses on to classify the brain tumor as yes (tumor infected) or no (not infected from tumor). The detection of brain tumor will be considered as a classification problem and it is evaluated using various state-of-the art algorithms i.e. random forest, Artificial Neural Network (ANN) and Decision-Tree (D T) based mining algorithms. This dissertation has improved the accuracy of mining rate further by ensemble the random forest with Decision-Tree. The simulation result shows that proposed method has better results as compared to the existing technique by using various parameters i.e. true Positive Rate, false positive rate, accuracy, f-measure, precision and recall .

Accuracy, Artificial Neural Network (ANN) and Decision-Tree (DT)

"Comparison of Data Mining techniques to detect the brain tumor", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 10, page no.96-103, October-2018, Available :http://www.jetir.org/papers/JETIR1810917.pdf

"Comparison of Data Mining techniques to detect the brain tumor", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 10, page no. pp96-103, October-2018, Available at : http://www.jetir.org/papers/JETIR1810917.pdf