Diabetes Prediction: using Ensemble Learning

The Diabetes Prediction Model using Ensemble Learning is a powerful tool implemented using ensemble learning techniques to predict whether an individual is likely to have diabetes. The model achieves an impressive accuracy of 99% and is trained on the "Diabetes.csv" dataset, which contains various health-related attributes and diabetes status information. The ensemble model combines three top-performing classifiers, namely RandomForestClassifier, XGBClassifier, and LGBMClassifier, using the VotingClassifier technique to improve prediction accuracy.

• High Accuracy: The diabetes prediction model achieves an impressive accuracy of 99%, making it a reliable tool for assessing an individuals risk of diabetes.
• Ensemble Learning: The model employs ensemble learning, which combines the strengths of multiple classifiers (RandomForestClassifier, XGBClassifier, and LGBMClassifier) to make more accurate predictions compared to individual models.
• User-Friendly Web Application: The live demo offers a simple and intuitive web interface where users can input their attributes and obtain predictions quickly and conveniently.
• Model Comparison: The application enables users to compare the performance of various classifiers against the ensemble model. Users can choose from a range of classifiers, including Naïve Bayes, Logistic Regression, K-Nearest Neighbors, Decision Tree, Gradient Boosting, LightGBM, Multilayer Perceptron, Artificial Neural Network, and Support Vector Machine.
• Dataset Information: The model is trained on the "Diabetes.csv" dataset, which contains diverse health attributes and corresponding diabetes status. Class labels are transformed into numerical format (0 for "N" - No Diabetes, and 1 for "Y" - Yes Diabetes) for training.
• Powerful Tool in Healthcare and Research: The models high accuracy and comparison capabilities make it a valuable tool for healthcare professionals and researchers in predicting diabetes risk and exploring different prediction models.