An automated insulin delivery app for iOS, built on LoopKit

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.

End-to-end diabetes risk prediction with KNN, SVM, RF, and ANFIS. Full pipeline, model export, visual reports, and an AI-powered risk assessment tool.

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

Predict Diabetes using Machine Learning.

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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.

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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.

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Personal project using Pima Indians Diabetes to analyse it and make predictions using Machine Learning techniques.

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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Medical Corner is a medical application that detection of pneumonia and brain tumor. It also provides prediction for heart disease, and diabetes.

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)

Streamlit Web App to predict the onset of diabetes based on diagnostic measures

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

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Diabetes prediction V2.0 -This VB6 application takes glycemic values and tries to predict the future state of the patient. First, it converts a sequence of numbers into states. The states are arranged in a transition matrix and the transition probabilities are calculated for each element. The transition matrix is further used for a predictions.

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

A Web App for Heart Disease Prediction, Diabetes Prediction and Breast Cancer Prediction.

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.

AI-powered healthcare platform combining Machine Learning for multi-disease prediction (Diabetes, Heart, Liver, Kidney, Lungs) with Generative AI for intelligent medical assistance and lab report analysis.

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Diabetes prediction V1.0 uses the Markov Chains method. First, this VB6 application converts a sequence of numbers into states. The states are arranged in a transition matrix and the transition probabilities are calculated for each element. Next, the transition matrix is further used for a prediction in a Markov chain.