Breast cancer has the second highest mortality rate in women next to lung cancer. As per clinical statistics, 1 in every 8 women is diagnosed with breast cancer in their lifetime. However, periodic clinical check-ups and self-tests help in early detection and thereby significantly increase the chances of survival. Invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, there arises the need for a more robust, fast, accurate, and efficient non-invasive cancer detection system. Early detection can give patients more treatment options. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. Then, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. AI will become a transformational force in healthcare and soon, computer vision models will be able to get a higher accuracy when researchers have the access to more medical imaging datasets. The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective. We have developed a computer vision model to detect breast cancer in histopathological images. Two classes will be used in this project: Benign and Malignant

We used different machine learning approaches to build models for detecting and visualizing important prognostic indicators of breast cancer survival rate. This repository contains R source codes for 5 steps which are, model evaluation, Random Forest further modelling, variable importance, decision tree and survival analysis. These can be a pipeline for researcher who are interested to conduct studies on survival prediction of any type of cancers using multi model data.

This project is a simple machine learning pipeline using Python and scikit-learn to predict breast cancer patient survival based on clinical data. The model uses the K-Nearest Neighbors (KNN) algorithm to make predictions.

This project aims to assist in the early detection of breast cancer using Machine Learning (ML) techniques. Early diagnosis is crucial for improving survival rates, and this project provides a simple yet effective web-based prediction app that can classify whether a breast tumor is malignant or benign based on input medical data.

Survival prediction study of breast cancer patients based on multiple machine learning models

AI-powered classification model for breast cancer diagnosis and survival prediction using machine learning techniques on clinical data.

The Breast Cancer Survival Prediction project aims to develop a machine learning model to accurately predict the likelihood of survival for breast cancer patients based on their clinical and demographic information.

This project focuses on Breast Cancer Prediction using machine learning. The dataset used is the Breast Cancer dataset from Scikit-Learn. Early detection of breast cancer is crucial in improving treatment success and patient survival rates.

Breast Cancer Survival Prediction This project focuses on predicting breast cancer patient survival using machine learning techniques. It involves data preprocessing, class balancing with NearMiss, exploratory data analysis (EDA), and hyperparameter tuning for a Random Forest Classifier using GridSearchCV.

Welcome to the Breast Cancer Survival Prediction repository! This project focuses on predicting breast cancer survival using the METABRIC dataset. It employs various machine learning models and techniques to analyze clinical and genetic data, aiming to provide insights into patient outcomes.

Comprehensive breast cancer data analysis on 4,024 patient records. Performs exploratory analysis, statistical testing, machine learning classification, clustering, and survival prediction using Python, Scikit-learn, and ensemble methods.