Evaluating the Impact of Different Feature Scaling Techniques on Breast Cancer Prediction Accuracy
Journal Title: Advance Knowledge for Executives - Year 2024, Vol 3, Issue 1
Abstract
Objective: To investigate the influence of different feature scaling techniques on the performance of machine learning algorithms in breast cancer prediction and identify the optimal combination of algorithm and scaler that yields the highest predictive accuracy. Method: Machine Learning Models (SVM, AdaBoost and RF), Feature Scaling Techniques (StandardScaler, MinMaxScaler, RobustScaler and Normalizer) Result: Effect of Feature Scaling. For SVM, feature scaling improved the performance. The best accuracy (98.25%) was obtained with MinMaxScaler. AdaBoost's performance remained consistent (~97.66%) across all scaling techniques. RF showed minor variations in performance across different scalers, but the differences were marginal. Conclusion: By experimenting with different combinations, practitioners can optimise model performance, ensuring more reliable and accurate predictions. Recommendation & Implication: Considering more than 30 features using a larger dataset in further study. Fine-tuning might lead to different results, testing the model with real-world data and exploring other preprocessing methods.
Authors and Affiliations
Chitcharoen, E. , Suwanwijit, N. , Mongkonchoo, K. , Utakrit, N. , Nuchitprasitchai, S. , & Bhumpenpein, N.
Keywords
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