Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.

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

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In this tutorial, i will apply a bunch of various Machine Learning Algorithms on the Breast Cancer Dataset and see how each of them behaves with respect to one another.

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.

predict whether the tumor is Malignant or Benign.

Machine Learning Class Project - Spring '22: Breast Cancer Prediction from Mammograms using Autoencoders to solve class imbalance problem

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.

Breast Cancer Prediction using 8 classification algorithm : Logistic Regression,Support Vector Machine(linear kernel),Support Vector Machine(polynomial kernel),Ensemble Learning Method of Decision Tree,Random Forest,Adaboost Classifier, and lastly voting algorithm based on Logistic Regression,Support Vector Machine(polynomial kernel) and Decision tree. Finally project presented with Python Graphical User Interface using the 2 algorithms having the maximum accuracy : Support Vector Machine(polynomial kernel) and Logistic Regression

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.

Breast Cancer Prediction with Machine Learning using Tensorflow.js

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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 prediction using basic machine learning methods

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.

This repository demonstrates the use of Logistic Regression, Random Forest, and XGBoost for breast cancer classification. It covers data preprocessing, hyperparameter tuning, and model evaluation with ROC-AUC and SHAP values, showcasing key skills in healthcare data analytics.

The objective of this project is to identify an approximately accurate model to predict the incidence of breast cancer based on various patients' clinical records.

This project leverages machine learning to classify breast cancer as malignant or benign based on tumor characteristics. By applying and evaluating multiple algorithms, the model achieves high accuracy, demonstrating the practical application of data-driven solutions in medical diagnostics.

I have created " 4 Machine Learning Models " of Breast Cancer Prediction and the algorithm I have selected for it are :- Logistic Regression , Decision Tree Classifier , Random Forest Classifier & Support Vector Classifier

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Breast-Cancer-Prediction-Using-Machine-Learning

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Breast Cancer Prediction by using Machine Learning