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breast-cancer-dataset

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In this work, we propose a deterministic version of Local Interpretable Model Agnostic Explanations (LIME) and the experimental results on three different medical datasets shows the superiority for Deterministic Local Interpretable Model-Agnostic Explanations (DLIME).

  • Updated Jul 6, 2023
  • Jupyter Notebook
Machine-Learning-Using-Python

Breast cancer diagnoses with four different machine learning classifiers (SVM, LR, KNN, and EC) by utilizing data exploratory techniques (DET) at Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD).

  • Updated Jul 9, 2022
  • Jupyter Notebook

A machine learning-based web app that predicts whether a breast tumor is Benign or Malignant using 29 medical features. Users can input data manually or upload a PDF report for automatic feature extraction. Built with Flask, Bootstrap, and PyMuPDF.

  • Updated Apr 20, 2026
  • Jupyter Notebook

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