OPTIMIZATION OF MACHINE LEARNING MODEL ACCURACY FOR BRAIN TUMOR CLASSIFICATION WITH PRINCIPAL COMPONENT ANALYSIS
Abstract
The main issue in brain tumor classification is the accuracy and speed of diagnosis through medical imaging. This study aims to improve the accuracy of machine learning models for brain tumor classification by using Principal Component Analysis (PCA) for dimensionality reduction. The research methods include image preprocessing, feature scaling, PCA application, and the implementation of machine learning algorithms such as Logistic Regression, Random Forest, Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes. The dataset consists of 3,264 images divided into training and testing sets. The results show that the use of PCA has varying impacts on different algorithms. PCA increases the accuracy of the SVM algorithm from 81% to 83% and KNN from 68% to 71%, but decreases the accuracy of Logistic Regression from 77% to 69% and Naive Bayes from 49% to 42%. Evaluation is performed using the Confusion Matrix and AUC-ROC to measure model performance. In conclusion, selecting the appropriate algorithm and preprocessing method is crucial in medical image classification, and the use of PCA should be considered based on the characteristics of the data and the algorithms used. This study also encourages the exploration of alternative dimensionality reduction methods for medical image analysis.
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