Diabetes Prediction

Project Domain: Healthcare Analytics

This repository contains the dataset and code for predicting diabetes using machine learning algorithms.

Overview

This project focuses on developing machine learning models to accurately predict the likelihood of diabetes in individuals based on various health-related features. By analyzing a comprehensive dataset, the goal is to create predictive models that can assist healthcare professionals in identifying at-risk individuals for early intervention.

The project involves several key steps:

• Data Preprocessing: Cleaning and preparing the dataset for analysis.
• Feature Selection: Identifying the most relevant features that contribute to accurate predictions.
• Model Training and Evaluation: Building and evaluating multiple machine learning models to determine which one offers the best performance in predicting diabetes.
• Model Comparison: Comparing the performance of different algorithms to select the most effective model for this task.

The dataset used in this project is the "Diabetes Prediction Dataset" from Kaggle, which includes features such as age, gender, BMI, hypertension, heart disease, smoking history, HbA1c level, and blood glucose level.

Dataset URL: Diabetes Prediction Dataset

Algorithms Used

The following machine-learning algorithms are employed in this project:

• Logistic Regression
• Decision Tree
• Support Vector Machine
• Random Forest
• XGBoost

Understand Features

• Age: As individuals age, the risk of developing diabetes increases due to factors such as reduced physical activity, changes in hormone levels, and a higher likelihood of developing other health conditions.

• Gender: Gender influences diabetes risk, with factors such as gestational diabetes in women or slightly higher prevalence in men contributing to differences in risk.

• Body Mass Index (BMI): Higher BMI is strongly associated with an increased risk of type 2 diabetes, as excess body fat can lead to insulin resistance.

• Hypertension: High blood pressure often coexists with diabetes, with both conditions sharing common risk factors and potentially exacerbating each other.

• Heart Disease: There is a bidirectional relationship between heart disease and diabetes, with each condition increasing the risk of developing the other.

• Smoking History: Smoking increases the risk of type 2 diabetes by contributing to insulin resistance and impairing glucose metabolism.

• HbA1c Level: HbA1c measures long-term blood sugar control, with higher levels indicating a greater risk of developing diabetes.

• Blood Glucose Level: Elevated blood glucose levels are a key indicator of impaired glucose regulation, which is a major risk factor for diabetes.