/Improved-machine-learning-based-Predictive-Models-for-Breast-Cancer-Diagnosis

Breast cancer diagnoses with four different machine learning classifiers (SVM, LR, KNN, and EC) by utilizing data exploratory techniques (DET) at Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD).

Primary LanguageJupyter NotebookThe UnlicenseUnlicense

Improved Machine Learning-based Predictive Models for Breast Cancer Diagnosis

This work proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. Breast cancer is diagnosed with four different machine learning classifiers (SVM, LR, KNN, and EC) at following two public datasets.

WDBC data availability: https://ftp.cs.wisc.edu/math-prog/cpo-dataset/machine-learn/cancer/WDBC/

BCCD data availability: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Coimbra


Citation Request:

Rasool, A.; Bunterngchit, C.; Tiejian, L.; Islam, M.R.; Qu, Q.; Jiang, Q. Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis. Int. J. Environ. Res. Public Health 2022, 19, 3211. https://doi.org/10.3390/ijerph19063211.

Online Published Paper