🧬 Diabetes Risk Analysis using R (MASS Package)

📌 Project Overview

This project investigates biological and clinical risk factors associated with Type-2 Diabetes using the Pima Indians dataset from the MASS package in R.

🧪 Biological Background

Type-2 Diabetes is a metabolic disorder influenced by plasma glucose levels, body mass index (BMI), age, and blood pressure. Understanding these factors is crucial for epidemiology and public health research.

📊 Dataset

• Source: MASS package (Pima.tr, Pima.te)
• Population: Adult female Pima Indians
• Response Variable: Diabetes status (Yes/No)

🎯 Objectives

• Identify significant biological risk factors
• Apply logistic regression
• Interpret results in life-science context
• Validate the predictive model

🧰 Tools & Skills

• R Programming
• Biostatistics
• Logistic Regression
• Data Visualization
• Biomedical Data Interpretation

🔬 Methods

1. Exploratory Data Analysis (EDA)
2. Visualization of clinical variables
3. Logistic regression modeling
4. Model validation on test data

📈 Key Findings

• Plasma glucose is the strongest predictor of diabetes
• BMI significantly increases disease risk
• Age shows moderate association
• Results align with known metabolic disease mechanisms

📁 Repository Structure