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.

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

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.

HealthOrzo is a Disease Prediction and Information Website. It is user friendly and very dynamic in it's prediction. The Project Predicts 4 diseases that are Diabetes , Kidney Disease , Heart Ailment and Liver Disease . All these 4 Machine Learning Models are integrated in a website using Flask at the backend .

A liver disease prediction I did using SVM classifier, Logistic regression and Random forest. The aim was to compare which of the classifiers give a better result in terms of the accuracy, recall, f1-score and precision. The dataset I used was gotten from https://www.kaggle.com/uciml/indian-liver-patient-records

This repository consist of various machine learning models along with the dataset. The models are trained with widely used ML algorithms like Gradient Boost , Random Forest etc. Pickle is used to serialize ML algorithms for predictions or availing it for the server use.

The goal of this project is to build regression models to predict the occurrence of cancer and liver disease using the National Health and Nutrition Examination Survey (NHANES) data.

Data Analysis and Machine Learning Techniques for Liver Disease Prediction