An automated insulin delivery app for iOS, built on LoopKit

Health Check ✔ is a Machine Learning Web Application made using Flask that can predict mainly three diseases i.e. Diabetes, Heart Disease, and Cancer.

In this project, the objective is to predict whether the person has Diabetes or not based on various features like Number of Pregnancies, Insulin Level, Age, BMI.

Predict Diabetes using Machine Learning.

Machine learning approach to detect whether patien has the diabetes or not. Data cleaning, visualization, modeling and cross validation applied

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.

A ready-to-use framework of the state-of-the-art models for structured (tabular) data learning with PyTorch. Applications include recommendation, CRT prediction, healthcare analytics, anomaly detection, and etc.

This project aims to predict the type 2 diabetes, based on the dataset. It uses machine learning model,which is trained to predict the diabetes mellitus before it hits.

I used six classification techniques, artificial neural network (ANN), Support Vector Machine (SVM), Decision tree (DT), random forest (RF), Logistics Regression (LR) and Naïve Bayes (NB)

Diabetes prediction with several machine learning algorithms to choose which is best.

This repo contains 4 different projects. Built various machine learning models for Kaggle competitions. Also carried out Exploratory Data Analysis, Data Cleaning, Data Visualization, Data Munging, Feature Selection etc

Diabetes Prediction is my weekend practice project. In this I used KNN Neighbors Classifier to trained model that is used to predict the positive or negative result. Given set of inputs are BMI(Body Mass Index),BP(Blood Pressure),Glucose Level,Insulin Level based on this features it predict whether you have diabetes or not.

This website provides a platform for users to predict their likelihood of developing diabetes based on various factors.

💉🤒 The project was built using maven project utilized WEKA in building the decision tree model and JavaFX in building GUI 💉🤒

Deployed medical apps on streamlit

Multiple Disease Prediction System

Prediction of diabetes using logistic regression

Diabetes Prediction with Logistic Regression

Exploring the relationship between PPG signals and Type 2 Diabetes.