Prediction of breast Cancer

This project file demonstrates a machine learning algorithm used for predicting Breast Cancer by taking in several variables and predicting if the breast cancer is benign or malign.

The aim of the project is developing to predictive modelfor detection, whether a breast cancer patient is going to survive the disease or not. It's a colloborated works with the contributers Kopy91 and bjoernmergarten.

Supervised Learning Algorithms

The key challenge in cancer detection is how to classify tumors into malignant or benign using machine learning. Early diagnosis can significantly increases the chances of survival of Breast cancer patient. In this case study, the task is to classify tumors into malignant or benign tumors using features from several cell images.

Developed using Python and Google Collab Notebook, this project leverages a Simple Multilayer Perceptron Neural Network (Feed Forward model) for breast cancer prediction. It utilizes the sklearn library for , and model evaluation. The dataset used is the Breast Cancer Wisconsin (Diagnostic) Data Set, Accuracy-95%

Breast cancer prediction using supervised learning algorithms is one of the many famous applications of machine learning. I have used logistic regression to predict whether a given tumor is malignant or benign.

Breast cancer prediction both in classification and clustering method for better understanding the data. Though clustering is different from classification,to finding the key aspect the data have,sometimes we need every possible way to catch behavior of the data.

predicting breast cancer using machine learning models

Breast cancer prediction using Random Forest, Light GBM, and XGBoost methods.

A simple implementation of the Logistic Regression Classifier on the Breast Cancer Dataset with L1 regularization and GridSearch for hyperparameter tuning.

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.

Using the SVM model to predict the accuracy of diagnosing breast cancer.

Breast Cancer Prediction with Hybrid Filter-Wrapper Feature Selection

Predicts whether the type of breast cancer is Malignant or Benign

Breast Cancer Prediction using Deep Learning and Keras

Using KNN classifier to detect whether the tumor is benign or malignant.

Developed an AI model which has the ability to detect whether the given person has breast cancer on the basis of mammogram images that is input by user. All this is contained in a web app.

A Machine learning project predicting the stage of breast cancer-malignant and benign using different machine learning algorithm