this project involves the development of SVM and ANN to predict breast cancer based on mean radiological features.

Breast Cancer Detection and Prediction using Machine Learning ... Project: Research on Medical Domain using AI and ML ... allowing for more effective treatment to be used and reducing the risks of death from breast cancer.

Multiple Disease Prediction System: An ML-based tool for early disease detection (Diabetes, Heart, Parkinson’s, Liver, Hepatitis, Lung Cancer, Kidney, Breast Cancer). Uses a Streamlit interface with trained models (.sav, .json) for risk prediction. Includes a Healthcare Chatbot for assistance.

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This project implements multiple machine learning algorithms to predict breast cancer diagnoses based on medical diagnostic data. The project compares the performance of various models, providing insights into which algorithms are most effective for this task.

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Using multiple Machine Learning models to predict the type tumor, by analyzing the tumor data

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.

Breast Cancer Early Predictions with Medical Report given as input in pdf or docx format , The medical report features gets automatically detected using OCR and get feed into ML algorithm for predictions

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.

College project on Breast Cancer Analysis and Prediction using ML

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TNBC Prediction Model 🧬: A complete ML pipeline to predict Triple Negative Breast Cancer using TCGA & Figshare gene expression data. Includes preprocessing, PCA, XGBoost modeling, hyperparameter tuning, external validation, and interpretability. Robust & generalizable.

This project explores ML techniques across classification and regression. It includes penguin species classification, breast cancer prediction, and baseball performance prediction using regularization. After, I will develop an XGBoost model for hotel cancellation prediction, analyzing key booking factors and optimizing performance. (In Progress)

This study compares ML models—Random Forest, KNN, SVM, and Logistic Regression—using features from the Wisconsin breast cancer dataset. After baseline testing with all 30 features, optimized subsets are selected using PSO, ACO, GA, DE, and WOA. Results show these optimized features greatly improve tumor malignancy prediction accuracy.

This repository contains the project "Breast Cancer Prediction using Machine Learning"

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Breast Cancer prediction using ML

A breast cancer prediction using ML

Breast-cancer-prediction using ml algorithms

Breast Cancer prediction using ML techniques

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Breast Cancer Detection Using Machine Learning is a project that applies machine learning algorithms to identify and classify breast cancer as malignant or benign based on medical data. By analyzing patterns in tumor features, this model supports early diagnosis and enhances decision-making in medical diagnostics.

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The goal of the project is to create predictive models that can classify breast masses as malignant or benign using predefined characteristics.

To determine whether the breast cancer is MALIGNANT OR BENIGN based on the given dataset

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Project on Breast Cancer Prediction using PyCaret using Machine Learning .