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.

PredictiX is a comprehensive multi-disease prediction system built using the MERN stack and integrated with machine learning models. It accurately predicts lung cancer, breast cancer, diabetes, and heart disease, providing a seamless user experience for health diagnostics.

# Breast-cancer-risk-prediction > Necessity, who is the mother of invention. – Plato* ## Welcome to my GitHub repository on Using Predictive Analytics model to diagnose breast cancer. --- ### Objective: The repository is a learning exercise to: * Apply the fundamental concepts of machine learning from an available dataset * Evaluate and interpret my results and justify my interpretation based on observed data set * Create notebooks that serve as computational records and document my thought process. The analysis is divided into four sections, saved in juypter notebooks in this repository 1. Identifying the problem and Data Sources 2. Exploratory Data Analysis 3. Pre-Processing the Data 4. Build model to predict whether breast cell tissue is malignant or Benign ### [Notebook 1](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB1_IdentifyProblem%2BDataClean.ipynb): Identifying the problem and Getting data. **Notebook goal:Identify the types of information contained in our data set** In this notebook I used Python modules to import external data sets for the purpose of getting to know/familiarize myself with the data to get a good grasp of the data and think about how to handle the data in different ways.  ### [Notebook 2](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB2_ExploratoryDataAnalysis.ipynb) Exploratory Data Analysis **Notebook goal:  Explore the variables to assess how they relate to the response variable** In this notebook, I am getting familiar with the data using data exploration and visualization techniques using python libraries (Pandas, matplotlib, seaborn. Familiarity with the data is important which will provide useful knowledge for data pre-processing) ### [Notebook 3](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB3_DataPreprocesing.ipynb) Pre-Processing the data **Notebook goal:Find the most predictive features of the data and filter it so it will enhance the predictive power of the analytics model.** In this notebook I use feature selection to reduce high-dimension data, feature extraction and transformation for dimensionality reduction. This is essential in preparing the data before predictive models are developed. ### [Notebook 4](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB4_PredictiveModelUsingSVM.ipynb) Predictive model using Support Vector Machine (svm) **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I construct a predictive model using SVM machine learning algorithm to predict the diagnosis of a breast tumor. The diagnosis of a breast tumor is a binary variable (benign or malignant). I also evaluate the model using confusion matrix the receiver operating curves (ROC), which are essential in assessing and interpreting the fitted model. ### [Notebook 5](https://github.com/ShiroJean/Breast-cancer-risk-prediction/blob/master/NB_5%20OptimizingSVMClassifier.ipynb): Optimizing the Support Vector Classifier **Notebook goal: Construct predictive models to predict the diagnosis of a breast tumor.** In this notebook, I aim to tune parameters of the SVM Classification model using scikit-learn.

A Machine Learning research tool that can be used to scan x-ray mammogram images for breast cancer. 30+ Features are extracted from the uploaded image through an image scanner and sent to the model for prediction. For more information, read through the Readme.md

This personal project incorporates a machine learning model to detect breast cancer using a dataset by scikit-learn. By using Logistic Regression the model is trained to classify tumors to either a malignant (cancerous) class or a benign (non-cancerous) class, offering reliable predictions for simple binary medical classification tasks.

This project analyzes breast cancer data to predict tumor malignancy using machine learning models, including regression and classification techniques. It features data visualization, preprocessing, and an interactive Streamlit app for exploration and prediction.

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Project for Prediction Breast Cancer Prediction for Classification Problem using Machine Learning Models.

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 implements a machine learning model to predict whether a breast tumor is malignant or benign based on a set of features extracted from the digital images of breast mass

Breast Cancer Outcome Prediction using Machine Learning. This project predicts Pathological Complete Response (PCR) and Relapse-Free Survival (RFS) in breast cancer patients using clinical and MRI-based features. It explores preprocessing, feature selection, and ML models, with XGBoost emerging as the best performer.

NeuralScan is an intelligent medical analysis system built with Python and machine learning. It integrates multiple ML models for breast cancer and diabetes prediction, featuring an analytical dashboard, model selection control, and a smart guidance module for research and educational use.

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The Breast Cancer Prediction Model is a machine learning application designed to predict whether a breast tumor is cancerous or non-cancerous based on a set of measurements. It utilizes a trained machine learning model built using Python and the Flask web framework.

MIT Breast Cancer Diagnosis Machine Learning Project: Uses various Machine Learning methods to develop prediction models that accurately diagnose breast cancer lesions.

Multiple Disease Prediction System is a supervised Machine Learning Model in Python. I have also deployed this machine learning web app using Streamlit. This web app can predict the diseases of a human such as Diabetes, heart disease, Parkinson's and Breast Cancer using Machine Learning model.

Early prediction and diagnosis of Breast cancer can prevent its spread and can also aid in effective treatment or medication. Predicting Breast cancer can be a very arduous task as the data can be highly Non-linear and may require high level computation modelling. However, many machine learning algorithms like KNN, K-Means, Decision Trees, Neural Networks etc. have proved to be effective in predicting Breast cancer. This study shows the using of K Nearest Neighbor (KNN) and K-Means algorithm to predict whether a person is having Breast cancer or not using a machine learning model trained with different features. Thus, we inferred that we can predict the Breast Cancer with reasonable accuracy.

This project predicts if someone has Breast Cancer or not by analyzing the tumor present as Benign or Malignant by taking into account various diagnosis features like radius mean, texture mean , perimeter mean , area mean , smoothness mean , compactness mean , concavity mean , concave points mean , symmetry mean , fractal dimension mean , radius se , texture se , perimeter se , area se , smoothness se , compactness se , concavity se , concave points se , symmetry se , fractal dimension se , radius worst , texture worst , perimeter worst , area worst , smoothness worst , compactness worst , concavity worst , concave points worst , symmetry worst and fractal dimension worst . This project uses Data Science and Machine Learning using Python. We use Linear Regression model for the prediction and Streamlit Library for the deployment.

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Machine learning models for breast cancer survival prediction using NCR data

Breast cancer prediction using machine learning. using python, tkinter and deep learning keras model

The project titled “Breast Cancer Prediction using Logistic Regression” is a machine learning–based analytical model designed to assist in the early detection and classification of breast cancer.

Prediction of Breast Cancer using ML models like Logistic Regression, Random Forest, Naive Bayes Classifier, KNN model and XGBoost. We have used Breast Cancer Wisconsin (Diagnostic) Data Set by UCI Machine Learning.

Breast Cancer Prediction using Machine Learning This project builds a machine learning classification model to predict whether a breast tumor is malignant or benign using diagnostic medical features. Multiple models were trained and evaluated to support early cancer detection.

The project aims to build a machine learning model for breast cancer prediction. It uses the Wisconsin Breast Cancer Diagnostic dataset to classify tumors as malignant (cancerous) or benign (non-cancerous).

Breast cancer prediction using machine learning involves the development of algorithms and models that analyze medical data, such as mammograms and patient information, to predict the likelihood of an individual having breast cancer.

Breast Cancer Prediction using Machine Learning A research-driven machine learning project by Neeraj and Divyanshu focused on early and accurate breast cancer detection using the Random Forest Classifier. This model leverages diagnostic datasets and streamlit-based interfaces to provide intuitive and reliable predictions. Achieved ~91% accuracy.

End-to-end machine learning project for breast cancer risk prediction using the METABRIC dataset, including data preprocessing, model training, and web deployment.