An Investigative Approach To Employ Different Machine Learning Algorithms in Detecting Brain Cancer

Abstract

This thesis takes an exploratory method to investigate the performance of several machine learning algorithms in more accurately detecting brain tumors. It primarily accomplishes this by detecting both malignant and benign lesions in the brain. In recent years, brain cancer has been linked to the highest newborn cancer fatality rates worldwide. Various machine learning techniques have been shown to be an excellent tool for detecting brain while it is still in its early stages. To train and test the model classifier, a dataset containing 700 instances and 1500 features from the Kaggle Data repository was used. Thirteen Eleven machine learning algorithms were studied and their performance parameters like confusion matrix and accuracy were analyzed. Furthermore, a thorough comparison was carried out through the compuration of precision, sensitivity, F1 score, recall, specificity, cross validation score and error rate of each algorithm.