Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis.

Abdur Rasool,Luo Tiejian,Md Ruhul Islam,Qiang Qu,Chayut Bunterngchit,Qingshan Jiang

doi:10.3390/ijerph19063211

Abstract

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we 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. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.

Highlights

Introduction published maps and institutional affilBreast cancer (BC) is the world’s leading cause of death in women after lung cancer, with approximately 2,261,419 new cases and 684,996 new deaths in 2020 [1]
We investigated four prediction models (SVM, logistic regression (LR), k-nearest neighbor (KNN), and ensemble classifier (EC)) with the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) breast cancer datasets, which reached the level of quality by diagnosing the tumor and classifying it into benign and malignant
Four-layered essential data exploratory techniques were proposed with four different machine learning predictive models, including support vector machine (SVM), LR, KNN, and ensemble classifier, to detect breast cancer tumors and classify them into benign and malignant tumors

Summary

Introduction

Breast cancer (BC) is the world’s leading cause of death in women after lung cancer, with approximately 2,261,419 new cases and 684,996 new deaths in 2020 [1]. 281,550 new cases were diagnosed with breast cancer, and 43,600 deaths were reported in the females during 2021 [2]. Breast cancer is a type of cancer that originates from breast tissue, most generally from the internal layer of the milk conduit or the lobules that provide milk to the milk conduit. Cancer cells arise from natural cells due to modification or mutation of deoxyribonucleic acid (DNA) and ribonucleic acid (RNA). These modifications or mutations may occur spontaneously as a result of the increase in entropy, or they may be triggered by other factors. Benign and malignant are two classes of iations

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