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
Related Articles
- EP ID EP731718
- DOI -
- Views 61
- Downloads 0
The Relationship between Engagement, Motivation, Cognitive Process, Student Satisfaction, and Effectiveness of Distance Education among a Higher Education University's Students
Objective: With the sudden COVID-19 shift away from the classroom in many parts of the world, some question whether adopting online learning or distance education will persist post-pandemic and how such a shift would aff...
The Impact of Talent Management on Employee Satisfaction and Business Performance in the Digital Economy : A Qualitative Study in Bangkok, Thailand
Objective: This study explains the impact of talent management on employee satisfaction and business performance in the digital economy in Bangkok, Thailand. Methods: The interview questions were developed based on ac...
A Book Review: Principle of Economics (Asst. Prof. Tanpat Kraiwanit, Ph.D.)
Objective: This review aims to analyse the principles of economics from Principles of Economics, written by Assistant Professor Tanpat Kraiwanit, Ph.D., and explores related articles to each topic, such as critical think...
Tourist Behavior and Intention to Revisit the Religious Sites: The Case of Cagayan Valley Region, Philippines
Objective: This study aims to evaluate and assess the tourist behavior and intention to revisit the religious sites in Cagayan Valley Region as related to the social environment, religious sites facilities, food and beve...
Predicting Electricity Consumption at La Trobe University Using Machine Learning Algorithms
Objective: This study aims to predict and optimise electricity consumption patterns at La Trobe University's Bundoora Campus from 2018 to 2021. The research involves rigorous feature extraction and model evaluation using...