Machine Learning-based Disease Diagnosis in Healthcare: A Focus on Brain Tumor Detection

One of the most crucial concerns facing society is healthcare for individuals. It searches out the most accurate and trustworthy illness diagnosis possible to make sure that patients get the care they need as soon as possible. Because this identification is usually sophisticated, other fields like statistics and computer science are needed for the search-related health component. To adopt cutting-edge approaches, these disciplines must go beyond the conventional ones. The actual number of new strategies makes it feasible to provide a thorough summary without getting into specifics. We suggest a detailed investigation of illnesses connected to machine learning in humans to achieve this. This study focuses on existing machine learning growth techniques used in the medical profession to diagnose human ailments in order to find fascinating trends, make unimportant forecasts, and aid in decision-making. Preparing the data is the initial step in every machine learning problem. This procedure involves the use of brain tumor classification datasets for training and testing a model. The dataset comprises four types of human brain tumor images, namely glioma tumor (gt), meningioma tumor (mt), no tumor (nt), and pituitary tumor (pt). The primary stage of a machine learning project is data cleaning. The quality of our data determines how well our machine learning model performs. Before supplying the data to the model for training, cleaning is always necessary. When the information has been gathered and sanitised. These cleaned data will be used to train the Support Vector Classifier (SVM), Linear Regression Classifier, and Logistic Regression Classifier. Using a confusion matrix, the models' quality will be evaluated. After training all the models, the predictions from each model will be combined to anticipate the disease based on the symptoms provided as input. In this proposed method, no tumor result achieved almost 100 percent accuracy in this specific dataset. Pituitary tumor data is also nearly 100 percent accurate.