UTILIZAÇÃO DE MACHINE LEARNING PARA PREVER A RECORRÊNCIA DE TUMORES CEREBRAIS

Abstract

Tumors can cause distress in people close to them, and in the process of investigating the subject, discoveries about related topics can be found. This work seeks to use machine learning to predict the recurrence of brain tumors in patients based on some attributes of these patients. This study was developed using Machine Learning techniques, used a Kaggle database, and the development steps were carried out in Google Colab, using the Python language and several specialized libraries. This prediction can help doctors and patients make more informed decisions about the treatment and monitoring of the disease. First, data preprocessing was done, which involved the application of one-hot encoding to the Treatment column, which contains three distinct categories (Radiation, Surgery, and Chemotherapy). This transformation is necessary to convert categorical data into a numerical format that can be interpreted by Machine Learning algorithms. This study demonstrated the effectiveness of using Machine Learning techniques, specifically neural networks, to predict the recurrence of brain tumors. With an accuracy of 98.67%, F1 score of 99.04% and recall of 99.28%, the model proved to be highly accurate, indicating that the use of Machine Learning can be a powerful tool in medicine, helping doctors and patients in making more informed decisions about the treatment and monitoring of brain tumors.