Multilayer Perceptron Neural network for binary classification between two type of breast cancer ("benign" and "malignant" )using Wisconsin Breast Cancer Database

🩺 Advanced neural network for breast cancer classification using Wisconsin dataset. Analyzes cell nucleus characteristics from FNA samples to distinguish malignant/benign masses with 96.5% accuracy. Features comprehensive documentation, automated setup, testing framework, and deployment guides. Educational ML project with 15,000+ lines of docs.

It is a Python based machine learning model which classifies breast cancer tumors as malignant or benign using the UltraSound Images, featuring data preprocessing, model training, and evaluation.

Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer

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.

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Rule-based expert system for prediction of a patient's survival within 5 years of undergoing breast cancer surgery & its comparison with ML classification techniques.

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Technicians can use a microscope to observe tissue samples that were taken from patients who are suspected to have breast cancer. By looking at the size and shape of the nuclei present within these tissue samples, one can then predict whether a given sample appears to be cancerous. In this document I demonstrate an automated methodology to predict if a sample is benign or malignant given measurements of nuclear shape that were made from digital images of fine needle aspirates of breast tissue masses from clinical samples.

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.

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

ML Classification and Segmentation for Breast Cancer

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

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An exploration of machine learning techniques applied to classify breast cancer as malignant or benign.

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Feedforward Neural Network Classification for Breast Cancer Wisconsin (Original) Data Set avaliabe in https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original)

Developed a ML model using Logistics regression to diagnose Breast cancer Wisconsin diagnosis dataset achieved accurate classification upto 97%

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.

This project explores ML techniques across classification and regression. It includes penguin species classification, breast cancer prediction, and baseball performance prediction using regularization. After, I will develop an XGBoost model for hotel cancellation prediction, analyzing key booking factors and optimizing performance. (In Progress)

A ML project utilizing CNN for breast cancer detection through image processing. Achieved an accuracy of 97% using a dataset from Kaggle, where images were manually structured and processed for feature extraction with CNN, followed by classification using SVM.

Breast cancer is one of the most common cancers among women worldwide, representing the majority of new cancer cases and cancer-related deaths according to global statistics, making it a significant public health problem in today’s society. The early diagnosis of it can improve the prognosis and chance of survival significantly, as it can promote timely clinical treatment to patients. Further accurate classification of benign tumors can prevent patients undergoing unnecessary treatments. Thus, the correct diagnosis of Breast Cancer and classification of patients into malignant or benign groups is the subject of much research. Because of its unique advantages in critical features detection from complex Breast Cancer datasets, machine learning (ML) is widely recognized as the methodology of choice in Breast cancer pattern classification and forecast modelling. Classification and data mining methods are an effective way to classify data. Especially in medical field, where those methods are widely used in diagnosis and analysis to make decisions. Because we are categorizing whether the tissue is cancerous or benign, we will train multiple Tree-based models for this procedure. We’ll experiment with hyper-parameters to see if we can enhance the accuracy. Try to solve the problem using the approach outlined below. For further information on each feature, consult the data dictionary. Decision trees (DTs) form the basis of ensemble algorithms in machine learning. These are powerful algorithms that can fit complex data. In this project, our focus is on understanding the core concepts of the Decision Tree for healthcare analysis, followed by understanding the different ensemble techniques.

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.

Over the past few decades, ML techniques have been widely used in intelligent healthcare systems, especially for breast cancer (BC) diagnosis and prognosis. Traditionally the diagnostic accuracy of a patient depends on a physician’s experience. however, this expertise is built up over many years of observations of different patient’s symptoms and confirmed diagnoses. ML techniques can take over some complex manual works from the physicians. Recently, ML techniques are playing a significant role in diagnosis of BC by applying classification techniques to identify people with BC, distinguish benign from malignant tumours and to predict weather the patient is affected or not. We focus on the neural network (NN), support vector machine (SVMs) and k-nearest neighbor (k-NNs) techniques in BC diagnosis.

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.

Description of the Project: + The "Breast Cancer Dataset" is used in this project. It has df.shape=(569, 31) which means 569 rows and 32 columns. + The link of the datset used in this project is -https://www.kaggle.com/uciml/breast-cancer-wisconsin-data + I am importing the important python packages- skelarn, pandas, numpy, seaborn and matplotlib to complete the project. + The machine learning models such as Logistic Regression, Decision Tree, Random Forest, XGBoost, AdaBoost and Gradient Boosting classifier have been used. + The performance of the machine learnig models have been tested on the basis of accuracy score, confusion matrix, classification report, f1 score and roc auc score. + I had tuned hyperparameters to improve the perforamnce for XGBoost model + The good visualization is also important along with accuracy score in model building. The performance of the model have been visualized in this project. Problem statement: The full form of XGBoost is eXtreme Gradient Boosting, also called winner for several kaggle competetion machine learning model. Most of the literatues of Machine Learning found in google has described this model as having best accuracy, efficient and feasibility. It is a decision-tree-based ensemble ML algorithm based on gradient boosting framework. It is considered that XGBoost provides a convenient way of cross-validation. Cross-validation technique is applied to test the model's overfitting during the training phase. If the model gives good accuracy in training dataset but the model works very poor in testing unseen dataset then it is called overfitting or a model of low bias and high variance. I have to calculate the model training and testing errors with different learning rates.As we know that the best technique to choose the learning rate value is between 0 and 1. I will be going to start the test by putting the learning rate as 0.01. It would easy to see the results through good visualization. I am also going to visualize the training and testing errors and accuracies by making a graph. Finally, I will tune the hyperparameters which helps us predict the testing datasets i.e. x_test.

Breast Cancer Classification Using AI/ML Algorithms

Breast cancer classification project using Machine Learning (Logistic Regression, Random Forest, KNN, SVM)

Machine learning project to correctly predict whether a tumor is malignant or benign.