Accuracy Improvement in Chondrosarcoma Detection using Decision Tree Algorithm (DT) and Comparing with Support Vector Machine (SVM)

This research is based on the improvement of accuracy in detection of chondrosarcoma the decision tree is taken and comparing the results with the support vector machine from deep learning algorithms. The total set of samples taken for these processes are 38 to analyze the two groups. Group 1 for the decision tree consisting of 19 samples and Group 2 for the support vector machine also containing 19 samples. The dataset has been imported and python code is implemented using Google Collab software. The sample has been calculated from the values obtained from the prior studies with help of some online statistical analysis tools with the pretest power of 80% and the alpha of 0.05 value. From simulation results of machine learning algorithms, the decision tree gives output accuracy of 95% which provides better output than the other used machine algorithm in this research support vector machine gives output of accuracy 81% (p < 0.05).For the above used dataset decision tree algorithm provides significantly more effective results than support vector machine from machine learning.