This repository contains all the data analytics projects that I've worked on in python.

Classification of Breast Cancer diagnosis Using Support Vector Machines

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.

Official code for Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning

Machine learning classifier for cancer tissues 🔬

Breast Cancer Detection classifier built from the The Breast Cancer Histopathological Image Classification (BreakHis) dataset composed of 7,909 microscopic images.

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides, BCNB Dataset

This CNN is capable of diagnosing breast cancer from an eosin stained image. This model was trained using 400 images. It has an accuracy of 80%

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.

This project uses mammograms for breast cancer detection using deep learning techniques.

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.

🩺 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.

Breast cancer classification from Mammogram images using Deep CNN with Keras and TensorFlow.

Breast cancer detection using 4 different models i.e. Logistic Regression, KNN, SVM, and Decision Tree Machine Learning models and optimizing them for even a better accuracy.

Predicting the Stage of Breast Cancer - M (Malignant) and B (Benign) using different Machine learning models and comparing their performance.

A machine learning process to distinguish good from bad breast cancer.

Breast Cancer Prediction using fuzzy clustering and classification

Breast cancer detection using machine learning with deployment of model

The idea is to estimate the chance of developing Breast Cancer. Providing advanced Data Insights. This helps women to understand the need for care. This comes with supposed Informatic Applications as a presentation for Mobile Phone's and Windows & MacOS Operating Systems in order for helping out in understanding the need for prior care.

Sistem Cerdas Prediksi Penyakit Kanker Payudara

A comprehensive machine learning-based web app for predicting multiple diseases from medical data.

Using the Knn algorithm, it detects whether the tumor is benign or malignant in people diagnosed with breast cancer.

Pluralistic Image Completion for Anomaly Detection (Med. Image Anal. 2023)

predict whether the tumor is Malignant or Benign.

Python feed-forward neural network to predict breast cancer. Trained using stochastic gradient descent in combination with backpropagation.

A text-based computational framework for patient -specific modeling for classification of cancers. iScience (2022).

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

In this machine learning project I will work on the Wisconsin Breast Cancer Dataset that comes with scikit-learn. I will train a few algorithms and evaluate their performance. I will use ipython (Jupyter).

Breast cancer diagnoses with four different machine learning classifiers (SVM, LR, KNN, and EC) by utilizing data exploratory techniques (DET) at Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD).

Breast Cancer Detection