A Machine Learning and Deep Learning based webapp used to predict multiple diseases.

PyTorch implementation of Grouped SSD (GSSD) and GSSD++ for focal liver lesion detection from multi-phase CT images (MICCAI 2018, IEEE TETCI 2021)

Medical Diagnosis A Machine Learning Based Web Application

An Open-access Dataset for Liver Lesion Diagnosis on Multi-phase MRI

CirrMRI600+: Large Scale MRI Collection and Segmentation of Cirrhotic Liver

Hepatic Disease (Liver Disease) is a broad term that encompasses various conditions affecting liver function, including cirrhosis, fatty liver disease, hepatitis, and liver cancer.

CirrMRI600+: Large Scale MRI Collection and Segmentation of Cirrhotic Liver

Predicting liver disease in patients using Machine Learning

A rule-based algorithm enabled the automatic extraction of disease labels from tens of thousands of radiology reports. These weak labels were used to create deep learning models to classify multiple diseases for three different organ systems in body CT.

This project aims to reduce the time delay caused due to the unnecessary back and forth shuttling between the hospital and the pathology lab. Here a machine learning algorithm will be trained to predict a liver disease in patients using a data-set collected from North East of Andhra Pradesh, India.

This is a Liver Disease Machine Learning Classification Capstone Project in fulfillment of the Udacity Azure ML Nanodegree. In this project, you will learn to deploy a machine learning model from scratch. The files and documentation with experiment instructions needed for replicating the project, is provided for you.

This project aims to predict liver disease in Indian patients

No description provided.

This project comprises predicting different types of disease at one place Pneumonia, Malaria, Liver Disease and Cardiovascular Disease

An AI multi-model system for liver disease using clinical data.

Heart failure and Liver disease risk assessment using the Naïve-Bayes Classification Algorithm

Library to compute 3D surface-distances for evaluating liver ablation/tumor completeness based on segmentation images.

The Multiple Disease Prediction System is a promising tool for enhancing healthcare accessibility and efficiency. By integrating advanced machine learning techniques with user-friendly interfaces, the system bridges the gap between technology and healthcare.

This webapp predict the whether the person have diabetes,heart disease,liver disease,kidney disease , back pain,tuberculosis.

Who is a Liver Patient?

This repository demonstrates the usage of a Random Forest Model to predict patients with liver disease using MATLAB.

This repository includes my Liver Disease Machine Learning-Flatiron School Module 3 Project. For this project I used libraries such as Pandas, Matplotlib, and Seaborn for visualizations and Scikit-Learn for the machine learning portion of the project. I implemented various classification algorithms on the data including some hyperparameter tuning.

A LIVER DETECTION PROGRAM TO RUN THE PROGRAM INSTALL THE PACKAGES FROM REQUIREMENT.TXT AND RUN app.py

An end-to-end Machine Learning + Flask web application that predicts Heart Disease, Liver Disease, Kidney Disease, and Breast Cancer using trained ML models and a modern UI.

LiverGuard-ML_Disease_Predictor is a machine learning project designed for early detection and diagnosis of liver diseases. It leverages advanced preprocessing, feature engineering, and evaluates multiple models—including Random Forest and XGBoost—to achieve high prediction accuracy on a clinical dataset.

Intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) quantitative imaging biomarkers extraction from clinical liver MRI

This is the Solution for the competition https://dphi.tech/challenges/sds-bit-mesra-ml-contest-on-liver-disease-prediction/192/leaderboard/private/ where our team Dataminers was able to achieve 21st position outs in private lea of 120 teamderboard, We explored a lot of imputational and interpolation methods for the mising data and built the whole pipeline in PyTorch. Our task was to predict the Stage of the patient according to their medical reports.

Codes for my project called " Fatty Liver Classification and Scaling: from 0 to 2 using CNN." In this project, I've created my own neural network and trained it with the images of the kidneys with the fat level scaling from 0 to 2. With each given new data to the network, the programme indicates which level of fatness the liver is categorized.

Transcriptomic cross-species analysis of chronic liver disease reveals consistent regulation between humans and mice

Analysis of the NAFLD-LOCATE randomised trial