Data Mining Course Project - Diabetes Classification with XGBoost - Winter 2022

Open-Source Diabetes Classifier: an R package to classify diabetes status in Danish registers

Machine Learning project focused on diabetes prediction, showcasing data preprocessing, model training, and evaluation using Python and scikit-learn.

Here we detect diabetes based on some attribute values related to body

KLASIFIKASI DATA PENYAKIT DIABETES MENGGUNAKAN ALGORITMA DECISION TREE DAN PARTICLE SWARM OPTIMIZATION

Decision Support System Application with Machine Learning Approach in Diagnosis of Diabetes

Diabetic Retinopathy Grading with CLIP-Guided Prototypes

Machine Learning Diabetes Prediction using 4 Classifier Algorithms for Fitting the Data.

In this project, our goal is to Predict the onset of diabetes based on diagnostic measures using a Machine Learning algorithm. We are focusing on KNN Classifier for this problem.

Machine Learning project focused on diabetes prediction, showcasing data preprocessing, model training, and evaluation using Python and scikit-learn.

A machine learning project for classifying diabetes using various algorithms. It includes data preprocessing, feature engineering, and hyperparameter optimization.

A Multiclass Diabetes Classification project using Random Forest, XGBoost, and SVM. Features a unique gender-based segmentation strategy (Male vs. Female models) to improve prediction accuracy across 3 diagnostic classes.

A PyTorch-based ANN for Multi-class Diabetes Classification. Implements robust clinical data preprocessing and deep learning to categorize patients based on health metrics from Kaggle.

This project aims to create a model to predict whether a patient has diabetes from analysing the patient's features.

A machine learning project to classify diabetes using the Pima Indians dataset. It compares multiple models (Naive Bayes, Decision Tree, Random Forest, SVC) and evaluates them using accuracy, F1 score, and specificity.

This project focuses on predicting diabetes in patients using a Decision Tree Classifier. The model is trained on medical diagnostic data to classify whether a patient is diabetic or non-diabetic. The project demonstrates a complete machine learning pipeline from data loading to model evaluation and visualization.

Machine Learning assignments: Uber Fare Prediction using Linear Regression & Diabetes Classification using K-Nearest Neighbors (KNN) — built with scikit-learn on Google Colab.

A parallel implementation of the K-Nearest Neighbor (KNN) algorithm using NVIDIA CUDA for diabetes classification. This project compares CPU and GPU performance in terms of accuracy, execution time, and computational speedup using the BRFSS 2015 Diabetes Health Indicators dataset.

Scripts for DePICtion projection in CPRD