Breast cancer: A comparative review for breast cancer detection using machine learning techniques

Area of Science:

• Oncology and Medical Informatics
• Computational methods for disease detection

Background:

• Breast cancer is a leading global health concern for women, with rising incidence and mortality rates.
• Barriers like cultural, socioeconomic, and educational factors impede early diagnosis and adequate healthcare access.
• Delayed diagnosis significantly worsens patient outcomes and increases the overall burden of breast cancer.

Purpose of the Study:

• To review machine learning (ML) algorithms for enhancing breast cancer detection accuracy.
• To explore ML's potential for early identification of breast cancer in women.
• To support improved women's health outcomes through advanced diagnostic techniques.

Main Methods:

• Systematic review of various machine learning algorithms applied to breast cancer detection.
• Analysis of algorithm performance metrics focusing on accuracy and early detection capabilities.
• Exploration of computational approaches for analyzing complex breast cancer data.

Main Results:

• Identified multiple machine learning techniques demonstrating high accuracy in breast cancer detection.
• ML algorithms show significant potential for enabling earlier and more precise diagnoses.
• The review highlights the effectiveness of computational methods in improving diagnostic performance.

Conclusions:

• Machine learning offers a promising avenue for improving the accuracy and timeliness of breast cancer detection.
• Implementing ML tools can help overcome barriers to early diagnosis, leading to better patient outcomes.
• A collaborative approach integrating ML into healthcare strategies is vital for reducing breast cancer mortality.