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

Diabetes predictions application with gui

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.

In the beginning, the algorithm chooses k centroids in the dataset randomly after shuffling the data. Then it calculates the distance of each point to each centroid using the euclidean distance calculation method.

In this case, we train our model with several medical informations such as the blood glucose level, insulin level of patients along with whether the person has diabetes or not so this act as labels whether that person is diabetic or non-diabetic so this will be label for this case.

Diabetic classification based on retinal images

Diabetes Prediction System using support vector classifier and its deployment on local machine

A Flask web app to predict diabetes in a patient using the SVM ML model

Machine learning classification on Pima Indian UCI dataset

This project uses a Machine learning approach to detect whether the patient has diabetes or not using different machine learning algorithms.

WebApp para analizador de retinopatía mediante ML

An open-source software platform for managing diabetes using a closed-loop insulin delivery system. The platform uses machine learning algorithms and continuous glucose monitoring to automatically adjust insulin dosing, improving glycemic control and reducing the risk of hypoglycemia.

This repository contains script and DUK files for Ethan de Villiers' research on Classification Models under Beverley Shields and Angus Jones at University of Exeter, Diabetes Team.

Includes my work done in the field of ML especially in the medical domain

This project aims to analyze diabetes data using data management, captivating visualizations, and cutting-edge machine learning techniques to predict the presence of diabetes in individuals. Our robust dataset includes comprehensive health exam results and family history.

A software tool that uses machine learning techniques to predict whether a person has diabetes based on their medical data.