Similarity-first interpretability studio for breast tumor samples: pick a case, find its closest “twins” (benign/malignant look-alikes), visualize neighborhood structure, compare feature fingerprints, and run minimal-change counterfactual edits toward a target class. Educational demo only, not for diagnosis.

A MATLAB-based GUI tool that predicts diabetes risk using machine learning (Random Forest) on the Pima Indian dataset.

🎗️ Breast Cancer Detection using PyCaret - a low-code machine learning pipeline. This project leverages automation and explainability to classify malignant and benign tumors from the Breast Cancer Wisconsin dataset. Built for simplicity, transparency, and clinical insight.

An educational machine learning project on early sepsis prediction using ICU time-series data, focusing on real-world challenges like missing data, class imbalance, data leakage, and clinically meaningful evaluation.

This project predicts brain stroke risk using machine learning by analyzing medical and lifestyle factors. It includes data preprocessing, model training, and a simple web interface for real-time predictions. Designed for learning, research, and healthcare analytics, it demonstrates practical ML applications in disease-risk assessment.

This project is an interactive web application built using Streamlit that predicts whether a breast tumor is Benign or Malignant based on clinical cell nuclei measurements. The application uses a pre-trained Logistic Regression model trained on the Wisconsin Breast Cancer dataset.

AI-powered breast cancer malignancy risk estimator using PyTorch, FastAPI, and React. Built for research & education — not for clinical use.

🧬 Discover similar tumor cases with Tumor Doppelgänger Studio, a tool for interpreting medical data through case comparisons. Educational use only.