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.

Cancer is a collection of related diseases, in which some of the body’s cells begin to divide without stopping and spread into surrounding tissues. Regardless of the view of cancer may be, it is exaggerated and over-generalized. While a diagnosis of cancer may still leave patients feeling helpless and out of control, in many cases today there is cause for hope rather than a blinkered vision of survival. The basic aim of our project is to ensure that patients with a risk or borderline edge of getting cancer shall get themselves digitally scanned, that would eventually generate a report. This report shall achieve in alluding convoluted details regarding certain possible properties of tumours that could be sent for prediction so that they could immediately diagnose it if at all it is predicted to be malignant. The importance of classifying cancer patients into high or low-risk groups has led many research teams, from the biomedical and the bioinformatics field, to study the application of machine learning (ML) methods. Therefore, these techniques have been utilized as an aim to model the progression and treatment of cancerous conditions. In addition, the ability of ML tools to detect key features from complex datasets reveals their importance. Support vector machine has become an increasingly popular tool for machine learning tasks involving classification, regression or novelty detection. Training a support vector machine requires the solution of a very large quadratic programming problem. Up to now, several approaches exist for circumventing the above shortcomings and work well with the dataset. And besides, till now the project has confined its attempt to diagnose breast cancer only. In this way, we can affirm that the prognosis of cancer can be achieved, and accordingly, we can produce outputs for the same.

This proposed projec presents a comparison of six machine learning algorithms: XGBoost Classifier, Random Forest, KNN Classifier, Logistic Regression, SVM Classification, Decision Tree. Our research led to 94.96% accuracy.

Cancer prediction at an early stage is very crucial as the patient can then prepare for dealing with it. There are several Machine Learning models that help in predicting cancer by identifying samples of independent persons at high risk, facilitating the design and planning of cancer trials. These models use biomarkers like age, menopause, tumor-size, invnodes, breast, breast-quad dimensions to predict breast cancer. However, these models had major drawbacks of late prediction as well as low accuracy. So here presenting the system which uses gene expression profiles (genomic data) to predict breast cancer at an early stage. This model is built using different machine learning algorithms like a highly versatile support vector machine (SVM), Naive Bayes theorem, Decision tree and nearest neighbors approach to predict breast cancer using gene expression profiles.

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this project predict breast cancer type based on the attached features in the datasets

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Final Year Project

A Machine Learning Approach for Early Prediction of Breast Cancer

Machine learning algorithms for breast cancer prediction - A deep learning approach

Application of machine learning in breast cancer survival prediction using a multimethod approach

Breast Cancer Detection and Prognosis Prediction Using Multi-Modal Machine Learning: A Radiogenomics Approach

A Hybrid Machine Learning Approach For Enhanced Prediction Of Breast Cancer With Lasso Method For Feature Extraction

Integrative Survival Prediction in Breast Cancer Using Extracellular Matrix Protease Transcript Signatures and Clinical Variables: A Machine Learning Approach

Breast Cancer Dataset for Machine Learning This repository contains a labeled breast cancer dataset designed for classification and predictive modeling tasks in medical data analysis. The dataset is suitable for machine learning and deep learning approaches focused on breast cancer diagnosis and risk prediction.

This is an attempt to create a breast cancer prediction model using various machine learning approaches. The goal is to classify these samples as either benign or malignant, aiding in the diagnosis of breast cancer.

Breast cancer prediction can be approached using machine learning algorithms. By analyzing a dataset that includes various features related to breast cancer, such as age, tumor size, and lymph node status, a predictive model can be built.

A machine learning-based breast cancer classification project where multiple models were tested and a hybrid XGBoost-SVM approach was developed to enhance prediction accuracy and assist in early medical diagnosis.

This project demonstrates a **machine learning-based approach** for classifying breast cancer as *Benign* or *Malignant* using Python. It explores dataset analysis, feature correlations, and the implementation of classification algorithms for medical data prediction.

A dual-approach Breast Cancer Prediction system using Machine Learning and Deep Learning. Includes EDA, model comparison (KNN, RF, Logistic), ANN with Keras, and hyperparameter tuning using RandomizedSearchCV and Keras Tuner. Final ANN model achieves 99.12% accuracy.

PredictBC is a machine learning framework for breast cancer prediction using microbial and metabolic features. It includes complete script with preprocessing, feature selection, custom ensemble model, and evaluation metrics. The approach integrates metagenomic and metabolomic abundances data to identify key biomarkers.

Creating a breast cancer risk prediction model using Extreme Gradient Boosting (XGBoost) and Random Forest algorithms. The dataset used is from the UCI Machine Learning Repository. This approach includes the use of Random Forest and XGBoost methods, and the model achieves a classification accuracy of 74.73%