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GlucoSense: AI-Powered Diabetes Detection for Early Intervention

Overview

GlucoSense is an innovative, AI-driven platform developed to enable early detection of diabetes, offering a proactive approach to managing this chronic condition. By analyzing a wide range of health metrics—such as age, gender, symptom history, and other vital indicators—GlucoSense quickly identifies potential diabetes cases, often before traditional symptoms become apparent. With its machine learning algorithms, GlucoSense adapts to new data, increasing accuracy over time and providing personalized insights that support healthcare providers and patients in making timely decisions. The goal is to empower early intervention, which can significantly improve long-term outcomes and reduce complications associated with diabetes.

Objectives

  1. Early Detection: To identify signs of diabetes at an early stage using predictive modeling, which can lead to more effective treatment and management.
  2. Risk Assessment: To assess a user's risk of developing diabetes based on their input data, enabling informed decision-making for both individuals and healthcare providers.
  3. Scalability: To develop a model that can be implemented on a larger scale, supporting population-wide diabetes screening initiatives.

Key Features

  1. Data Preprocessing: Handles data cleaning, missing value treatment, and outlier analysis to ensure a robust dataset.
  2. Feature Engineering: Selects and engineers features that are most predictive of diabetes risk.
  3. Machine Learning Models: Trains and evaluates several models (e.g., Logistic Regression, Decision Trees, Random Forests) to determine the most accurate model for diabetes prediction.
  4. Balanced Dataset Handling: Implements techniques like oversampling to handle any imbalances in diabetes-positive and negative cases.
  5. Evaluation Metrics: Uses metrics such as accuracy, precision, recall, and F1-score to evaluate model performance and ensure high reliability.

Dataset

The dataset includes a variety of health indicators, including:

  • Age (numerical data)
  • Gender
  • Polyuria
  • Polydipsia
  • sudden weight loss
  • weakness
  • Polyphagia
  • Genital thrush
  • visual blurring
  • Itching
  • Irritability
  • delayed healing
  • partial paresis
  • muscle stiffness
  • Alopecia
  • Obesity
  • Diabetes Status (Target Variable)

Libraries and Frameworks

To run this project locally, ensure you have the following dependencies installed:

  • Python 3.x
  • Pandas: For data manipulation
  • NumPy: For numerical computations
  • Matplotlib and Seaborn: For data visualization
  • Scikit-learn: For machine learning algorithms and evaluation metrics

License

This project is licensed under the MIT License .

Acknowledgments

The dataset used is sourced from reliable and publicly available resources.
Thanks to the open-source community for providing essential tools and libraries.

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