This project focuses on predicting breast cancer using machine learning models through cross-validation techniques. The dataset utilized in this study is the Breast Cancer Wisconsin (Diagnostic) dataset.

Extreme gradient boosting algorithm (Xgboost)

Lung Cancer Prediction Model: Leverage the power of deep learning with this TensorFlow-based project. Trained on a dataset of lung X-Ray images, the model accurately predicts cancer cases. Easily integrate and utilize the model for early detection. #HealthTech #MachineLearning

The following repository consists of some Fundamental Data Science and Machine Learning Prediction Models created using available datasets from Github itself using Google Colab Notebook

Cancer Prediction using ML in MATLAB/Octave

TensorFlow/Keras examples and notes.

Breast Cancer Survival Prediction | Machine Learning Course Project, Fall 2022

Prediction of colorectal cancer (CRC) phenotype based on Microbiome Metagenomics

As part of this project, I have used Machine Learning (classification) algorithms for classification of tumors in Human Breasts as Non-Cancerous/ Benign or Cancerous/ Malignant tumors.

This repository presents a project describing a quantum simulator algorithm for early cancer prediction. The QisKit and QisKit Aer libraries were used for the experiment. At the core lies Python as the programming language.

SVM_ML

This repository houses a workflow that uses biological feature trees to segregate cancer RNA-seq datasets, then it trains machine learning models to predict the presence or absence of known, cancer-associated DNA-level mutations.

This Python Project utilizes the Pandas and Matplotlib Libraries, also a PrettyTable Module and this research project aims to provide a comprehensive overview of cancer cases and rates in the various states of the United States using various graphs and visualizations to better understand the trends and patterns over the years.

Empowering early cancer detection through advanced machine learning models. Our project focuses on predicting oral, cervical, and brain tumors using a blend of image and risk factor data. Join us in the journey to enhance healthcare outcomes through cutting-edge technology

Skin cancer prediction using input values for various parameters from Doctor

CARES (Cancer Awareness and Risk Evaluation by Self-Assessment) monitors real-time blood data through regular CBP (Complete Blood Picture) updates to assess cancer risk. It also offers comprehensive information on cancer symptoms, treatments, and preventive measures.

This project uses machine learning classifier algorithms to predict whether the patient is suffering from cancer or not.

This project consists of the analysis and use of Breast Cancer Wisconsin (Diagnostic) Data Set to predict the type of tumour in patients.

Supervised Learning Experiments on Wisconsin Breast Cancer Dataset

Breast Cancer Detection using Machine Learning