Machine Learning Patient Risk Analyzer Solution Accelerator is an end-to-end (E2E) healthcare app that leverages ML prediction models (e.g., Diabetes Mellitus (DM) patient 30-day re-admission, breast cancer risk, etc.) to demonstrate how these models can provide key insights for both physicians and patients. Patients can easily access their appointment and care history with infused cognitive services through a conversational interface. In addition to providing new insights for both doctors and patients, the app also provides the Data Scientist/IT Specialist with one-click experiences for registering and deploying a new or existing model to Azure Kubernetes Clusters, and best practices for maintaining these models through Azure MLOps.

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

Flask based web app with five machine learning models on the 10 most common disease prediction, covid19 prediction, breast cancer, chronic kidney disease and heart disease predictions with their symptoms as inputs or medical report (pdf format) as input.

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.

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

Breast Cancer Prediction with Machine Learning using Tensorflow.js

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Breast Cancer Prediction Model: A machine learning model that predicts breast cancer with an accuracy of 91.8%.

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.

Breast_Cancer_Prediction By Using Machine Learning with Flask

Early prediction and diagnosis of Breast cancer can prevent its spread and can also aid in effective treatment or medication. Predicting Breast cancer can be a very arduous task as the data can be highly Non-linear and may require high level computation modelling. However, many machine learning algorithms like KNN, K-Means, Decision Trees, Neural Networks etc. have proved to be effective in predicting Breast cancer. This study shows the using of K Nearest Neighbor (KNN) and K-Means algorithm to predict whether a person is having Breast cancer or not using a machine learning model trained with different features. Thus, we inferred that we can predict the Breast Cancer with reasonable accuracy.

Breast cancer (BC) is one of the most common cancers among women in the world, representing the majority of new cancer cases and deaths related to cancer according to global statistics, causing it a severe public health concern in today’s society. For 2019, it was estimated earlier by the Canadian Cancer Statistics that 26,900 Canadian women will be diagnosed with breast cancer, and 5,000 will die of it. Breast cancer accounts for approximately 25% of new cases of cancer and 13% of all cancer deaths in Canadian women. 1 in 8 women are expected to develop breast cancer during their lifetime, and 1 in 33 will die of it. While it can also be found in men, male breast cancer is an infrequent occurrence. Breast cancer starts in the cells of the mammary gland. Breast tissue covers a larger area than just the breast, extending up to the collarbone and fromthe armpit to the breastbone. A prediction of breast cancer inan initial stage provides a higher possibility of its cure. It needs a breast cancer prediction tool that can classify a breast tumorwhether it is a malignant tumor or a benign tumor. Machine learning (ML) is widely recognised as a technique of choice in BC pattern classification and forecast modelling due to its unique advantages in critical feature detection from complex BC datasets. Classification and data mining methods are an effective way to classify data. This work aims to show the working of different machine learning algorithms and compare the results of their performance accuracy to present an effective method for the prediction of breast cancer.

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.

Breast Cancer Prediction: Machine Learning-based Diagnosis with Streamlit

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Working on "Prediction of Malignant Breast Cancer Cases using Ensemble Machine Learning: A Case Study of Pesticides Prone Area" paper

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Breast Cancer prediction with Machine Learning

This web-based Breast Cancer Prediction system utilizes machine learning models to predict whether a tumor is malignant (cancerous) or benign (non-cancerous) based on user-provided medical data. The system visualizes the prediction results with an interactive pie chart showing the probability of Cancer

Explore our repository for research paper & code predicting breast cancer with machine learning. We utilize numerical datasets, applying PCA for efficient model training, ensuring accurate predictions. Join us in advancing healthcare with data-driven innovation.

Lung Cancer and Breast Cancer prediction site with Machine Learning model .

AI-powered breast cancer risk prediction with explainable machine learning

machine learning project : breast cancer prediction with logistics regression and support vector machine

Breast cancer classification and prediction with multi-view deep convolutional neural networks using machine learning

Breast Cancer Detection App using Streamlit A machine learning web application that predicts breast cancer based on input features. Built with Python, Streamlit, and scikit-learn. Users can input patient data and get predictions instantly.

Prediction the Survivability of Male Breast Cancer Respondents in Detroit Michigan Area by Machine Learning Methods and Compare with Bayesian Cox Proportional Regression Model

Flask based Web app with 5 Machine Learning Models including 10 most common Disease prediction and Coronavirus prediction with their symptoms as inputs and Breast cancer , Chronic Kidney Disease and Heart Disease predictions with their Medical report as inputs