Evaluation of feature scaling for improving the performance of supervised learning methods

Tsehay Admassu Assegie (Injibara University)
Vadivel Elanangai (St. Peter's Institute of Higher Education and Research)
Josephin Shermila Paulraj (R.M.K College of Engineering and Technology)
Mani Velmurugan (Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology)
Daya Florance Devesan (Velammal Engineering College)

Bulletin of Electrical Engineering and Informatics
Vol 12, No 3: June 2023

This article evaluates the performance of the support vector machine (SVM), decision tree (DT), and random forest (RF) on the dataset that contains the medical records of 299 patients with heart failure (HF) collected at the Faisalabad Institute of Cardiology and the Allied hospital in Pakistan. The dataset contains 13 descriptive features of physical, clinical, and lifestyle information. The study compared the performance of three classification algorithms employing pre-processing techniques such as min-max scaling, and principal component analysis (PCA). The simulation result shows that the performance of the DT, and RF decreased with dimensionality reduction while the SVM improved with dimensionality reduction. The SVM achieved 84.44%. Thus, feature scaling improves the performance of the SVM. The RF performs at 82.22%, the DT at 81.11%, and the SVM shows an improvement of 1.64% with scaled features, compared to the original dataset.