Machine learning is widely used in bioinformatics and particularly in breast cancer diagnosis. In this project, certain classification methods such as K-nearest neighbors (K-NN) and Support Vector Machine (SVM) which is a supervised learning method to detect breast cancer are used.

# 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.

This project analyzes breast cancer data using Python, employing libraries for data manipulation, visualization, and machine learning. The main objectives are to explore the dataset, visualize insights, and implement classification algorithms to predict breast cancer diagnoses.

A comprehensive machine learning application that predicts breast cancer malignancy using cytology measurements. Features an interactive Streamlit web interface with real-time visualizations including radar charts for cell nuclei analysis. Implements logistic regression with data preprocessing pipelines for accurate benign/malignant classification.

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.

Content Past Usage: Attributes 2 through 10 have been used to represent instances. Each instance has one of 2 possible classes: benign or malignant. Wolberg,~W.~H., \& Mangasarian,~O.~L. (1990). Multisurface method of pattern separation for medical diagnosis applied to breast cytology. In {\it Proceedings of the National Academy of Sciences}, {\it 87}, 9193--9196. -- Size of data set: only 369 instances (at that point in time) -- Collected classification results: 1 trial only -- Two pairs of parallel hyperplanes were found to be consistent with 50% of the data -- Accuracy on remaining 50% of dataset: 93.5% -- Three pairs of parallel hyperplanes were found to be consistent with 67% of data -- Accuracy on remaining 33% of dataset: 95.9% Zhang,~J. (1992). Selecting typical instances in instance-based learning. In {\it Proceedings of the Ninth International Machine Learning Conference} (pp. 470--479). Aberdeen, Scotland: Morgan Kaufmann. -- Size of data set: only 369 instances (at that point in time) -- Applied 4 instance-based learning algorithms -- Collected classification results averaged over 10 trials -- Best accuracy result: -- 1-nearest neighbor: 93.7% -- trained on 200 instances, tested on the other 169 -- Also of interest: -- Using only typical instances: 92.2% (storing only 23.1 instances) -- trained on 200 instances, tested on the other 169 Relevant Information: Samples arrive periodically as Dr. Wolberg reports his clinical cases. The database therefore reflects this chronological grouping of the data. This grouping information appears immediately below, having been removed from the data itself: Group 1: 367 instances (January 1989) Group 2: 70 instances (October 1989) Group 3: 31 instances (February 1990) Group 4: 17 instances (April 1990) Group 5: 48 instances (August 1990) Group 6: 49 instances (Updated January 1991) Group 7: 31 instances (June 1991) Group 8: 86 instances (November 1991) Total: 699 points (as of the donated datbase on 15 July 1992) Note that the results summarized above in Past Usage refer to a dataset of size 369, while Group 1 has only 367 instances. This is because it originally contained 369 instances; 2 were removed. The following statements summarizes changes to the original Group 1's set of data: Group 1 : 367 points: 200B 167M (January 1989) Revised Jan 10, 1991: Replaced zero bare nuclei in 1080185 & 1187805 Revised Nov 22,1991: Removed 765878,4,5,9,7,10,10,10,3,8,1 no record : Removed 484201,2,7,8,8,4,3,10,3,4,1 zero epithelial : Changed 0 to 1 in field 6 of sample 1219406 : Changed 0 to 1 in field 8 of following sample: : 1182404,2,3,1,1,1,2,0,1,1,1 Number of Instances: 699 (as of 15 July 1992) Number of Attributes: 10 plus the class attribute Attribute Information: (class attribute has been moved to last column) Attribute Domain Sample code number id number Clump Thickness 1 - 10 Uniformity of Cell Size 1 - 10 Uniformity of Cell Shape 1 - 10 Marginal Adhesion 1 - 10 Single Epithelial Cell Size 1 - 10 Bare Nuclei 1 - 10 Bland Chromatin 1 - 10 Normal Nucleoli 1 - 10 Mitoses 1 - 10 Class: (2 for benign, 4 for malignant) Missing attribute values: 16 There are 16 instances in Groups 1 to 6 that contain a single missing (i.e., unavailable) attribute value, now denoted by "?". Class distribution: Benign: 458 (65.5%) Malignant: 241 (34.5%) Acknowledgements O. L. Mangasarian and W. H. Wolberg: "Cancer diagnosis via linear programming", SIAM News, Volume 23, Number 5, September 1990, pp 1 & 18. William H. Wolberg and O.L. Mangasarian: "Multisurface method of pattern separation for medical diagnosis applied to breast cytology", Proceedings of the National Academy of Sciences, U.S.A., Volume 87, December 1990, pp 9193-9196. O. L. Mangasarian, R. Setiono, and W.H. Wolberg: "Pattern recognition via linear programming: Theory and application to medical diagnosis", in: "Large-scale numerical optimization", Thomas F. Coleman and Yuying Li, editors, SIAM Publications, Philadelphia 1990, pp 22-30. K. P. Bennett & O. L. Mangasarian: "Robust linear programming discrimination of two linearly inseparable sets", Optimization Methods and Software 1, 1992, 23-34 (Gordon & Breach Science Publishers). Inspiration Rouse Tek Bio informatics Cytogenomics Project is an attempt to bring the human genome to the understanding of how cancers develop. All of our bodies are composed of cells. The human body has about 100 trillion cells within it. And usually those cells behave in a certain fashion. They observe certain rules, they divide when they’re told to divide, they’re quiescent when they’re told to remain dormant, they stay within a particular position within their tissue and they don’t move out of that. Occassionally however, a single cell, of those 100 trillion cells, behave in a different way. That cell keeps dividing when all its signals around it tell it to stop dividing. That cell ignores its counterparts around it and pushes them out of the way. That cell stops observing the rules of the tissue within which it is located and begins to move out of its normal position, invading into the tissues around it and sometimes entering the bloodstream and becoming a metastasis, depositing in another tissue of the body.. The reason the cell has gone rogue is because it has acquired within its genome, within its DNA, a number of abnormalities that cause it to behave as a cancer cell. All 100 trillion cells in the human body have got a copy of the human genome, they have 2 copies, 1 maternal, 1 paternal. Throughout Life all those copies of the genome in those 100 trillion cells, are acquiring abnormal changes or somatic mutations. These mutations are present in the cell and are not transmitted from parents to offspring. They are constrained to that individual cell. Those mutations occur in every cell of the body, normal and abnormal, for a number of different reasons. They occur because every time a cell divides possibly one letter of code out of 3 billion is replicated incorrectly. And that’s 1 source of somatic mutations. Another source is that our 100 trillion cells are being exposed to a number of different onslaughts like radiation, self generated chemicals from inhalation of things like tobacco smoke or even an unhealthy diet over time. Occasionally mechanisms in a particular cell make breakdown and the DNA of that cell begins to acquire somatic mutations rather more commonly than other cells. So in summary, every cell in the body acquires mutations throughout a lifetime, and as we get older we acquire more and more somatic mutations in which occasionally a particular type of gene is mutated where the protein that it makes is abnormal and drives the cell to behave in a rogue fashion that we call cancer.

Use TCGA data to predict early/late pathologic stage of breast cancer with gene expression data using classification machine learning algorithm, train and test with multiple models, screen and evaluate significant genes from the model

This is a project using the Wisconsin Breast Cancer (Diagnostic) dataset from the UCI Machine Learning Repository. link: https://archive.ics.uci.edu/ml/datasets/Breast+Cancer+Wisconsin+(Diagnostic) I will compare different machine learning models (Logistic Regression, Support Vector Machines) to see what would provide the best classification results in differentiating malignant tumors from benign tumors.

Breast cancer detection using machine learning classification is a project where you build a model to identify whether a given set of medical features indicates the presence of breast cancer. This project involves using a labeled dataset of medical records, where each record is classified as either indicating breast cancer or not.

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.

Project for Prediction Breast Cancer Prediction for Classification Problem using Machine Learning Models.

Breast cancer classification using machine learning models with PCA and performance evaluation on a medical dataset.

Building a machine learning project named Breast Cancer Classification using Python 3.6, IPython Notebook and Python Virtual Environment

Machine Learning Breast Cancer Classification involves developing predictive models to classify breast cancer as benign or malignant based on clinical data, such as tumor size and cell features. Using algorithms like logistic regression, SVM, or neural networks, aiding early detection and improving patient outcomes.

Our project focuses on using machine learning classification algorithms to develop a breast cancer detection system. We gathered a diverse dataset and applied preprocessing techniques, feature selection, and various classification algorithms to train and evaluate our models.

Beginner-friendly Machine Learning classification project that predicts breast cancer as benign or malignant using Logistic Regression. Built with Python and scikit-learn, the model achieves ~96% accuracy and is evaluated using accuracy score and confusion matrix.

Breast cancer classification model using machine learning techniques in python

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This basic classification of breast cancer project implies concepts of Logistic Regression

Predicts and Classifies Cancer based on type of tumor as Benign and Malignant based on values from a Biopsy Report.

Classify breast tumors (malignant/benign) using Logistic Regression & Random Forest on the Breast Cancer Wisconsin dataset. Includes EDA, preprocessing, model evaluation, and feature importance. High accuracy achieved. Explore class imbalance techniques or top features next.

Artificial Neural Networks (ANN) and Decision Tree (DT) classifiers are used to develop a machine learning (ML) model using the Wisconsin diagnostic breast cancer (WDBC) dataset, so as to assess the characteristics of a breast cancer formation at early stages and classify it as benign or malignant. In the proposed scheme, feature selection and feature extraction are done to extract statistical features from the dataset and comparison between the models is provided based on their performance to identify the most suitable approach for diagnosis. The dataset apportioned into various arrangements of train-test split. The presentation of the framework is estimated, depending on accuracy, sensitivity, specificity, precision, and recall. The binary classification problem achieved a maximum accuracy of 98.55%. Paper accepted at IEEE conference.

End-to-end breast cancer classification using machine learning and SHAP explainability

Detection/Prediction of Breast cancer among females using Classification machine learning algorithms .Breast cancer here are of two type Malignant and Benign.

Classification using Machine Learning Techniques on Breast Cancer Wisconsin Data Set (diagnosis) Using Support Vector Machines with the accuracy of 0.96

This repository contains the code and conclusions from a Breast Cancer Detection Machine Learning project. This project using FNA imaging and classification models to determine if breast cancer cells are malignant or benign

Pinktober – Breast Cancer Classification (Datathon Project) Built a machine learning pipeline for breast cancer prediction using tabular health data. Achieved 98.2% accuracy in a 48-hour internal datathon organized by Micro Club.

This project implements a Breast Cancer Detection system using Principal Component Analysis (PCA) for dimensionality reduction and a machine learning model for classification.