https://github.com/armahdavi/ai_ml_assisted_breast_cancer_tumor_detection

Consolidating tutorial codes for breast cancer tumor detection, covering ML fundamentals like classification, feature engineering, training, evaluation, and key performance metrics.
https://github.com/armahdavi/ai_ml_assisted_breast_cancer_tumor_detection

bias-variance feature-engineering logistic-regression machine-learning machine-learning-algorithms medical-application numpy pandas python random-forest recall-precision sklearn xgboost-classifier

Last synced: 6 days ago
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Consolidating tutorial codes for breast cancer tumor detection, covering ML fundamentals like classification, feature engineering, training, evaluation, and key performance metrics.

• Host: GitHub
• URL: https://github.com/armahdavi/ai_ml_assisted_breast_cancer_tumor_detection
• Owner: armahdavi
• Created: 2024-07-07T14:43:12.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-22T19:44:40.000Z (8 months ago)
• Last Synced: 2025-02-22T20:26:38.292Z (8 months ago)
• Topics: bias-variance, feature-engineering, logistic-regression, machine-learning, machine-learning-algorithms, medical-application, numpy, pandas, python, random-forest, recall-precision, sklearn, xgboost-classifier
• Homepage:
• Size: 0 Bytes
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
## Breast Cancer Detection: A Practical Introduction to Machine Learning

Breast cancer tumor detection is one of the earliest and most impactful applications of Machine Learning (ML) in medical and biological sciences. It has played a crucial role in advancing early diagnosis, improving treatment outcomes, and reducing mortality rates. The ML-assisted breast cancer tumor detection is not only vital for research and clinical use but also a key learning tool for new ML learners. Many ML courses include breast cancer detection as a project or tutorial to introduce learners to key concepts and methodologies in ML.

Over the past few years, I have had the opportunity to tutor a few friends who were new to ML, guiding them through the fundamental principles of ML. Each time, I used breast cancer tumor detection as a practical example to explain essential ML concepts such as regression, classification, feature sets and space, target variables, cost functions, gradient descent, training and testing processes, and evaluation metrics, including accuracy, precision, recall (sensitivity), and receiver operating characteristic (ROC) curve analysis with area under the curve (AUC) scores.

In this repository, I decided to consolidate all the tutorial codes I developed while teaching these concepts.

I hope you find it useful!