Brain tumor prediction using naïve Bayes’ classifier and decision tree algorithms

  • Authors

    • Danda Shashank Reddy
    • Chinta Naga Harshitha
    • Carmel Mary Belinda
    2018-02-05
    https://doi.org/10.14419/ijet.v7i1.7.10634
  • Diagnosis, Treatment, Efficient, Prediction, Proposing
  • Now a day’s many advanced techniques are proposed in diagnosing the tumor in brain like magnetic resonance imaging, computer tomography scan, angiogram, spinal tap and biospy. Based on diagnosis it is easy to predict treatment. All of the types of brain tumor are officially reclassified by the World Health Organization. Brain tumors are of 120 types, almost each tumor is having same symptoms and it is difficult to predict treatment. For this regard we are proposing more accurate and efficient algorithm in predicting the type of brain tumor is Naïve Bayes’ classification and decision tree algorithm. The main focus is on solving tumor classification problem using these algorithms. Here the main goal is to show that the prediction through the decision tree algorithm is simple and easy than the Naïve Bayes’ algorithm.

  • References

    1. [1] [1] Janki naik 1, Sagar Patel 2 “Tumor Detection and Classification using Decision Tree in Brain MRIâ€IJCSNS (International journal of computer science and Network Security, Vol. 14 no.6, June 2014, pp.87-91.

      [2] Varun Jain Sunila Gondara, (june2017),Comparative Study of Data Mining Classification Methods in Brain Tumor Disease Detection.IJCSC, Vol.8, Issue-2 pp.12-17.

      [3] Kalyani A.Bhawar, Prof.Ajay S.chhajed “Brain Tumor classification using Data mining algorithms.IJESRT, November-2016, pp.239-243.

      [4] V.Vani, M.Kalaiselvi Geetha Automatic Tumor Classification of Brain MRI Images .IJCSC, Vol-4, Issue-10, pp.144-151.

      [5] Mr. Meena, E Murali Study on Various Machine Learning Algorithms for Brain Tumor Detection.Ijpam, Volume-117 No.8 2017, pp.139-143.

      [6] Chau, M.; Shin, D. (2009). A Comparative Study of Medical Data Classification Methods Based onDecision Tree and Bagging Algorithms. Proceedings of IEEE International Conference onDependable, Autonomic and Secure Computing, pp. 183-187.

      [7] Kearns M. and Mansour Y. On the boosting ability of top-down decision treelearning algorithms.Journal of Computer and Systems Sciences, 58(1): 109-128, 1999.

      Brain Tumor ppt. Dr.Walaa Nasr Lecturer of Medical-Surgical Nursing Department Second year 2012
  • Downloads

  • How to Cite

    Shashank Reddy, D., Naga Harshitha, C., & Mary Belinda, C. (2018). Brain tumor prediction using naïve Bayes’ classifier and decision tree algorithms. International Journal of Engineering & Technology, 7(1.7), 137-141. https://doi.org/10.14419/ijet.v7i1.7.10634