Improving Naïve Bayes Performance with Information Gain Using Machine Learning for Breast Cancer Classification
Peningkatan Performa Naïve Bayes dengan Information Gain Menggunakan Machine Learning untuk Klasifikasi Kanker Payudara
DOI:
https://doi.org/10.33998/jms.2025.5.2.2338Keywords:
Naïve Bayes; Information Gain; Classification; Breast CancerAbstract
This study aims to enhance the performance of the Naïve Bayes algorithm in breast cancer diagnosis classification by integrating the Information Gain feature selection method. The dataset used is the Wisconsin Breast Cancer (Diagnostic) dataset, consisting of 569 samples. This study evaluates the effectiveness of feature selection in improving the accuracy, sensitivity, and specificity of the classification model. The implementation of the Information Gain feature selection method successfully increased the Naïve Bayes model's accuracy from 94.15% to 96.49%, a 2.34% improvement. The addition of feature selection significantly enhanced the predictive capability of the model. The findings of this study can support more accurate medical decision-making, potentially influencing treatment decisions and patient outcomes in clinical practice. This research provides new insights into the application of machine learning in medical diagnostics and suggests directions for future research.
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