Open Source Breast Cancer Research for Summer Research Program ASIME 2024 @ Adelphi University, New York. Data from the UV Irvine Machine Learning Repository.
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Updated
Aug 12, 2024 - Jupyter Notebook
Open Source Breast Cancer Research for Summer Research Program ASIME 2024 @ Adelphi University, New York. Data from the UV Irvine Machine Learning Repository.
The MAMA-MIA Dataset: A Multi-Center Breast Cancer DCE-MRI Public Dataset with Expert Segmentations
This repository contains machine learning programs in the Python programming language.
Submission for CL4HEALTH @ LREC-COLING 2024
Decision Tree Algorithm written in Python with NumPy and Pandas
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).
Uses data analysis tools to investigate potential factors towards a patient’s survival of breast cancer. Such as, protein evaluation levels, types of surgery, stages of cancer, & patient’s age.
This Program is for Prediction of Breast Cancer
Medical Chatbot for Breast Cancer Care
This is a SteamLit Web-App which delves in Exploratory Data Analysis with Iris, Breast-Cancer and Wine datasets using ML models like KNN's, SVM's and Random Forests
Random Forest Algorithm written in Python using NumPy and Pandas
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).
L'analyse des composantes principales essaie de trouver les axes principaux qui sont des variables décorrélées qui décrivent au mieux nos données.
Week 10 - Intro to Linear Algebra in Python
[Big Data Analytics] This analysis aims to observe which features are most helpful in predicting malignant or benign cancer and to see general trends that may aid us in model selection and hyper parameter selection.
Experiments for machine learning lab (ETCS 402) have been added here.
XGBoost Model for Machine Learning implementation on breast cancer dataset in Python and churm modelling in R
Implementation of some classification and clustering methods
Decision Tree Classification was explored on Breast Cancer Data.
Machine learning algorithms using Numpy and Pandas
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