Machine Learning Patient Risk Analyzer Solution Accelerator is an end-to-end (E2E) healthcare app that leverages ML prediction models (e.g., Diabetes Mellitus (DM) patient 30-day re-admission, breast cancer risk, etc.) to demonstrate how these models can provide key insights for both physicians and patients. Patients can easily access their appointment and care history with infused cognitive services through a conversational interface. In addition to providing new insights for both doctors and patients, the app also provides the Data Scientist/IT Specialist with one-click experiences for registering and deploying a new or existing model to Azure Kubernetes Clusters, and best practices for maintaining these models through Azure MLOps.

Diabetes Risk Prediction System using Machine Learning and React. The project performs clinical risk assessment based on health and lifestyle inputs. Features include data preprocessing, ML classification, and a React UI with age ranges, tooltips, and risk-based outputs.

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Being the most common and rapidly growing disease, Diabetes affecting a huge number of people from all span of ages each year that reduces the lifespan. Having a high affecting rate, it increases the significance of initial diagnosis. Diabetes brings other complicated complications like cardiovascular disease, kidney failure, stroke, damaging the vital organs etc. Early diagnosis of diabetes reduces the likelihood of transiting it into a chronic and severe state. The identification and analysis of risk factors of different spinal attributes help to identify the prevalence of diabetes in medical diagnosis. The prevalence measure and identification of diabetes in the early stages reduce the chances of future complications. In this research, the collective NHANES dataset of 1999-2000 to 2015-2016 was used and the purposes of this research were to analyze and ascertain the potential risk factors correlated with diabetes by using Logistic Regression, ANOVA and also to identify the abnormalities by using multiple supervised machine learning algorithms. Class imbalance, outlier problems were handled and experimental results show that age, blood-related diabetes, cholesterol and BMI are the most significant risk factors that associated with diabetes. Along with this, the highest accuracy score .90 was achieved with the random forest classification method.

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A robust framework was proposed where outlier rejection, filling the missing values, data standardization, K-fold validation, and different Machine Learning (ML) classifiers were used. Finally, to improve the result, weighted ensembling of different ML models also proposed.

Train a machine learning model to predict diabetes status and deploy it to a flask based web app, with visuals using Plotly dash.

This project implements an end-to-end machine learning pipeline to predict diabetes based on medical attributes. It includes data preprocessing, exploratory data analysis, Logistic Regression model training, and evaluation using accuracy, confusion matrix, and classification report.

In this i've tried to predict the probability of a person having diabetes based on some data fields...

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Final year major project. AI based Diabetes prediction model using ML algorithms and HTML CSS Flask User Interface.

This web-based application uses machine learning (Logistic Regression) to predict whether a person is likely to have diabetes based on health parameters like glucose level, BMI, age, insulin, and more.

A smart assistant app for diabetes prediction and daily management using ML and Flutter.

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This project involves the comparison of performance of ML and DL algorithm for the prediction of diabetes.

Diabetes Prediction Project Using Machine Learning. This app is a simple web application using the Flask framework, where users can input health data (like glucose levels, BMI, etc.) to predict if they are diabetic or not based on a Logistic Regression model.

A ML & DL Diabetes Prediction Flutter App

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Machine learning project that predicts diabetes using patient medical data with a structured preprocessing and model evaluation pipeline built in Python.

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Diabetes prediction through ML in google colab

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Diabetes onset prediction using CART decision tree — healthcare ML with interpretability focus

Diabetes prediction with ML model and deployment with Django

This project uses Machine Learning techniques to predict whether a person is likely to have diabetes based on medical parameters. The goal of this project is to apply ML concepts to a real-world healthcare problem and deploy the model as an interactive web application.

This AI-powered Diabetes Prediction App uses a trained ML model to assess diabetes risk based on user inputs. Built with Streamlit, it provides an intuitive interface for entering clinical parameters like Glucose, BMI, Insulin, Blood Pressure, and Age for instant predictions.

Using ML workflow to process and transform data to create a prediction model. This model must predict which people are likely to develop diabetes with 70% or great accuracy,

This python file will compare accuracy of different ML and AI models on the Pima Indian Diabetes dataset and a small GUI to make predictions from the logistic regression model. This project was done as a part of Mini project for my sem 4 Engineering SE- IT