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From Data to Diagnosis: Breast Cancer Detection with Machine Learning

Utilising Binary, Supervised Machine Learning (Distance Based Algorithm i.e. KNN) and Using 5 Fold Nested Cross Validation for Robust Model Evaluation and Hyper-parameter Tuning

TLDR

  • This Jupyter notebook from the HealthyData.Science team implements a full supervised machine learning workflow using the Breast Cancer Wisconsin Dataset and a K‑Nearest Neighbours classifier to distinguish benign from malignant lesions.​

  • It combines exploratory data analysis, feature engineering, and 5‑fold nested cross‑validation to tune hyper‑parameters and assess diagnostic performance in a reproducible, code‑driven way.​

  • Evaluation should consider dataset suitability and licence, transparency of each modelling step in the notebook, and how such a distance‑based classifier could be integrated into existing diagnostic workflows and governance processes.

This project shows how breast cancer diagnosis was once approached with classical machine learning on curated datasets. Now, AI in medical imaging goes even further, reading scans in real-time, spotting patterns invisible to the human eye, and helping clinicians act faster with greater confidence.

Explore our curated list of AI solutions for medical imaging to see how industry leaders are accelerating timelines, implementing AI solutions in healthcare, and strengthening their competitive edge.

Stephen
Author: Stephen

Founder of HealthyData.Science · 20+ years in life sciences compliance & software validation · MSc in Data Science & Artificial Intelligence.

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