Implementation of Tumor Prediction System using Classification Algorithms

Abstract

As the huge volume of healthcare data was being unused, recent researchers were focused on predicting the many diseases by analyzing the past patient records. In continuation with that, there are lot of researches focused on predicting the tumor on the human body. In this research, two widely used classification algorithms called Naïve Bayes and Random tree were considered for implementation and analysis with the UCI Machine learning Tumor data set. The data cleaning technique called “Replace Missing Values” in the WEKA tool has been considered for cleaning the data. The implementation has been done with the original dataset and the cleaned dataset. Finally, it is found that the Random tree algorithm is performed well with improved accuracy and reduced error rate. The accuracy obtained before data cleaning is 90.8333% and after data cleaning is 93.3333 %. Similarly, the error rates were reduced reasonably and they are 9.1667 % before data cleaning and 93.3333 % after data cleaning. In future, the data cleaning techniques has to be tuned well to improve the accuracy further.

An Approach for Enhancement of MR Images of Brain Tumor

“…where, I1 is the original image and I2 is the enhanced image. The lowest value of mean absolute error shows better quality features [55]. The MAE values are very low in the proposed method compared to other state-of-the-art methods (Table 3).…”