Diabetes Detection Model

The Diabetes Detection Model is a machine learning-based system designed to predict the likelihood of diabetes in individuals based on clinical and lifestyle data. By analyzing key health indicators such as glucose levels, insulin, BMI, blood pressure, and age, the model assists in early diagnosis and risk assessment.

Key Features:
Data Processing & Feature Engineering: Cleans and preprocesses patient data to enhance model accuracy.
Machine Learning Algorithms: Uses Logistic Regression, Decision Trees, Random Forest, and Neural Networks for classification.
Model Evaluation: Assesses performance with accuracy, precision, recall, F1-score, and ROC-AUC.
Deployment: Integrated with Flask or FastAPI for real-time predictions in healthcare applications.
This model supports early detection, proactive intervention, and improved patient outcomes, assisting healthcare professionals in making data-driven decisions.

The Diabetes Detection Model is a machine learning-based system designed to predict the likelihood of diabetes in individuals based on clinical and lifestyle data. By analyzing key health indicators such as glucose levels, insulin, BMI, blood pressure, and age, the model assists in early diagnosis and risk assessment.

Key Features:
Data Processing & Feature Engineering: Cleans and preprocesses patient data to enhance model accuracy.
Machine Learning Algorithms: Uses Logistic Regression, Decision Trees, Random Forest, and Neural Networks for classification.
Model Evaluation: Assesses performance with accuracy, precision, recall, F1-score, and ROC-AUC.
Deployment: Integrated with Flask or FastAPI for real-time predictions in healthcare applications.
This model supports early detection, proactive intervention, and improved patient outcomes, assisting healthcare professionals in making data-driven decisions.