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Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

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Machine Learning Based Comparative Analysis for Breast Cancer Prediction.

Mohammad Monirujjaman Khan¹, Somayea Islam¹, Srobani Sarkar¹

¹Department of Electrical and Computer Engineering, North South University, Bashundhara, Dhaka-1229, Bangladesh.

Journal of Healthcare Engineering

|April 22, 2022

Summary

This summary is machine-generated.

Early breast cancer detection using machine learning models significantly improves survival rates. Logistic regression achieved 98% accuracy, outperforming other methods for identifying breast cancer.

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Area of Science:

Oncology
Medical Informatics
Machine Learning

Background:

Breast cancer is a leading cause of cancer in women, with increasing prevalence.
Early detection is crucial for effective treatment and improved patient survival.
Computer-aided detection and diagnosis (CAD) technologies aid in early breast cancer identification.

Purpose of the Study:

To evaluate machine learning models for breast cancer detection.
To leverage recent advancements in CAD systems and methodologies.
To identify the most effective machine learning model for breast cancer diagnosis.

Main Methods:

Utilized the Wisconsin Breast Cancer Diagnostic (WBCD) dataset.
Applied and analyzed multiple machine learning models: Random Forest, Logistic Regression, Decision Tree, and K-Nearest Neighbor.
Compared the performance of these models for breast cancer prediction.

Main Results:

Logistic regression demonstrated the highest accuracy at 98%.
The logistic regression model outperformed other tested machine learning methods.
The findings indicate the potential of machine learning in enhancing breast cancer diagnosis.

Conclusions:

Machine learning models, particularly logistic regression, show significant promise for accurate and early breast cancer detection.
Early detection through advanced computational methods can lead to better patient outcomes.
Further development and application of CAD systems can enhance breast cancer screening and diagnosis.